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Wei, Jianchong, Yi Wu, Liang Chen, Kunping Yang, and Renbao Lian. "Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model." Remote Sensing 14, no. 22 (November 13, 2022): 5737. http://dx.doi.org/10.3390/rs14225737.
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Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose a zero-shot RS image dehazing method based on a re-degradation haze imaging model, which directly restores the haze-free image from a single hazy image. Based on layer disentanglement, we design a dehazing framework consisting of three joint sub-modules to disentangle the hazy input image into three components: the atmospheric light, the transmission map, and the recovered haze-free image. We then generate a re-degraded hazy image by mixing up the hazy input image and the recovered haze-free image. By the proposed re-degradation haze imaging model, we theoretically demonstrate that the hazy input and the re-degraded hazy image follow a similar haze imaging model. This finding helps us to train the dehazing network in a zero-shot manner. The dehazing network is optimized to generate outputs that satisfy the relationship between the hazy input image and the re-degraded hazy image in the re-degradation haze imaging model. Therefore, given a hazy RS image, the dehazing network directly infers the haze-free image by minimizing a specific loss function. Using uniform hazy datasets, non-uniform hazy datasets, and real-world hazy images, we conducted comprehensive experiments to show that our method outperforms many state-of-the-art (SOTA) methods in processing uniform or slight/moderate non-uniform RS hazy images. In addition, evaluation on a high-level vision task (RS image road extraction) further demonstrates the effectiveness and promising performance of the proposed zero-shot dehazing method.
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Gu, Ziqi, Zongqian Zhan, Qiangqiang Yuan, and Li Yan. "Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network." Remote Sensing 11, no. 24 (December 13, 2019): 3008. http://dx.doi.org/10.3390/rs11243008.
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Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy remote sensing images, we propose to combine a haze density prior with deep learning, where an initial haze density map (HDM) is firstly extracted from the original hazy image, and is subsequently utilized as the input of the network, together with the original hazy image. Meanwhile, a large-scale hazy remote sensing dataset is created for training and testing of the proposed method, which contains both uniform and non-uniform, synthetic and real hazy remote sensing images. Experimental results on the created dataset illustrate that the developed dehazing method obtains significant progresses over the state-of-the-art methods.
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Wei, Jianchong, Yan Cao, Kunping Yang, Liang Chen, and Yi Wu. "Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning." Remote Sensing 15, no. 11 (May 24, 2023): 2732. http://dx.doi.org/10.3390/rs15112732.
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Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model training, the acquisition of these datasets is time-consuming and laborious, and the resulting models may encounter a domain shift when processing real-world hazy images. To overcome the limitations of prior-based and learning-based dehazing methods, we propose a self-supervised remote sensing (RS) image-dehazing network based on zero-shot learning, where the self-supervised process avoids dense dataset requirements and the learning-based structures refine the artifacts in extracted image priors caused by complex real-world environments. The proposed method has three stages. The first stage involves pre-processing the input hazy image by utilizing a prior-based dehazing module; in this study, we employed the widely recognized dark channel prior (DCP) to obtain atmospheric light, a transmission map, and the preliminary dehazed image. In the second stage, we devised two convolutional neural networks, known as RefineNets, dedicated to enhancing the transmission map and the initial dehazed image. In the final stage, we generated a hazy image using the atmospheric light, the refined transmission map, and the refined dehazed image by following the haze imaging model. The meticulously crafted loss function encourages cycle-consistency between the regenerated hazy image and the input hazy image, thereby facilitating a self-supervised dehazing model. During the inference phase, the model undergoes training in a zero-shot manner to yield the haze-free image. These thorough experiments validate the substantial improvement of our method over the prior-based dehazing module and the zero-shot training efficiency. Furthermore, assessments conducted on both uniform and non-uniform RS hazy images demonstrate the superiority of our proposed dehazing technique.
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Sun, Ziyi, Yunfeng Zhang, Fangxun Bao, Ping Wang, Xunxiang Yao, and Caiming Zhang. "SADnet: Semi-supervised Single Image Dehazing Method Based on an Attention Mechanism." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 2 (May 31, 2022): 1–23. http://dx.doi.org/10.1145/3478457.
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Many real-life tasks such as military reconnaissance and traffic monitoring require high-quality images. However, images acquired in foggy or hazy weather pose obstacles to the implementation of these real-life tasks; consequently, image dehazing is an important research problem. To meet the requirements of practical applications, a single image dehazing algorithm has to be able to effectively process real-world hazy images with high computational efficiency. In this article, we present a fast and robust semi-supervised dehazing algorithm named SADnet for practical applications. SADnet utilizes both synthetic datasets and natural hazy images for training, so it has good generalizability for real-world hazy images. Furthermore, considering the uneven distribution of haze in the atmospheric environment, a Channel-Spatial Self-Attention (CSSA) mechanism is presented to enhance the representational power of the proposed SADnet. Extensive experimental results demonstrate that the presented approach achieves good dehazing performances and competitive running times compared with other state-of-the-art image dehazing algorithms.
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Roy, Sangita, and Sheli Sinha Chaudhuri. "Fast Single Image Haze Removal Scheme Using Self-Adjusting." International Journal of Virtual and Augmented Reality 3, no. 1 (January 2019): 42–57. http://dx.doi.org/10.4018/ijvar.2019010103.
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At present the classical problem of visibility improvement is hot topic of research. An image formation optical model is presented where a clear day image has high contrast with respect to an image plagued with bad weather. A degraded daytime image has high intensity with minimum deviation among pixels in every channel. No reference digital image haze removal is a problem. The static haziness factor for all types of images cannot be applicable for effective haze removal. A minimum intensity channel of the three RGB channels is estimated as transmission of an image with a dynamic haziness factor to be a ratio of minimum to maximum pixel intensity of the hazy image. Adaptive contrast, extinction coefficient, the maximum visible distance of hazy images as well as dehazed images from each image are evaluated uniquely. The resulting high-quality haze free image with linear computational complexity O(n) is appropriate for real time applications. The effectiveness of the technique is validated by quantitative, and qualitative evaluations.
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Su, Chang, Wensheng Wang, Xingxiang Zhang, and Longxu Jin. "Dehazing with Offset Correction and a Weighted Residual Map." Electronics 9, no. 9 (September 1, 2020): 1419. http://dx.doi.org/10.3390/electronics9091419.
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In hazy environments, image quality is degraded by haze and the degraded photos have reduced visibility, making the less vivid and visually attractive. This paper proposes a method for recovering image information from a single hazy image. The dark channel prior algorithm tends to underestimate the transmission of bright areas. To address this problem, an improved dehazing algorithm is proposed in this paper. Assuming that intensity in a dark channel affected by haze produces the same offset, the expected value of the dark channel of a hazy image is used as an approximation of this offset to correct the transmission. However, this correction may neglect scene difference and affect the clarity of the recovered images. Therefore, a weighted residual map is used to enhance contrast and recover more information. Experimental results demonstrate that our algorithm can effectively lessen color oversaturation and restore images with enhanced details. This algorithm provides a more accurate transmission estimation method that can be used with a weighted residual map to eliminate haze and improve contrast.
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Bhadouria, Aashi Singh, and Khushboo Agarwal. "An Effective Framework for Enhancement of Hazed and Low-Illuminated Images." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 791–800. http://dx.doi.org/10.22214/ijraset.2022.40382.
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Abstract: Haze removal is important for computer photography and computer vision applications. However, most of the existing methods for removing theha- ziness are designed for daytime images and may not always work well at hazy night images. Unlike image conditions during the sunny day, images captured in winter night conditions can suffer from irregular lighting due to artificial light sources with varying colors and non-uniform illumination, which show low brightness, contrast and color distortion. In this paper, we propose a new frame- work for presenting night-time hazy imaging, which works on haze removal and low-illumination correction algorithm taking into consideration both the non-uni- form illumination of artificial light sources and the effects of dispersion and at- tenuation of fog. Therefore, firstly, we will give a hazy low-illuminated image having low light as input and then apply a technique to clarify the visibility of the input image. Then, apply the contrast enhancement and after that apply the LIME technique and finally, apply the white balance technique and we will get our improved output image. The experimental results show that the proposed algorithm can achieve an illumination balance, results without haziness and good color cor- rection capacity. Keywords: Image Enhancement, Low Illumination, Reflectance, Low Contrast, Low Light Images, Nighttime Images, Low Visibility Images, Nighttime Haze Removal.
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Hashim, Ahmed, Hazim Daway, and Hana kareem. "No reference Image Quality Measure for Hazy Images." International Journal of Intelligent Engineering and Systems 13, no. 6 (December 31, 2020): 460–71. http://dx.doi.org/10.22266/ijies2020.1231.41.
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Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using Transmission Component Estimation (TCE). This done by calculating the correlation coefficient between non-reference measures and subjective measure, the results show that the proposed method has a high correlation coefficient values for Pearson correlation coefficient (0.8923), Kendall (0.7170), and Spearman correlation coefficient (0.8960). The image database used in this work consists of 70 hazy images captured by using a special device, design to capture haze image. The experiment on haze database is consistent with the subjective experiment.
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KIM, Geun-Jun, Seungmin LEE, and Bongsoon KANG. "Single Image Haze Removal Using Hazy Particle Maps." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E101.A, no. 11 (November 1, 2018): 1999–2002. http://dx.doi.org/10.1587/transfun.e101.a.1999.
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Wang, Xuemei, Mingye Ju, and Dengyin Zhang. "Automatic hazy image enhancement via haze distribution estimation." Advances in Mechanical Engineering 10, no. 4 (April 2018): 168781401876948. http://dx.doi.org/10.1177/1687814018769485.
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Hsieh, Cheng-Hsiung, and Ze-Yu Chen. "Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models." Electronics 12, no. 16 (August 17, 2023): 3485. http://dx.doi.org/10.3390/electronics12163485.
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Recently, supervised deep learning methods have been widely used for image haze removal. These methods rely on training data that are assumed to be appropriate. However, this assumption may not always be true. We observe that some data may contain hazy ground truth (GT) images. This can lead to supervised deep image dehazing (SDID) models learning inappropriate mapping between hazy images and GT images, which negatively affects the dehazing performance. To address this problem, two difficulties must be solved. One is to estimate the haze level in an image, and the other is to develop a haze level indicator to discriminate clear and hazy images. To this end, we proposed a haze level estimation (HLE) scheme based on dark channel prior and a haze level indicator accordingly for training data cleaning, i.e., to exclude image pairs with hazy GT images in the data set. With the data cleaning by the HLE, we introduced an SDID framework to avoid inappropriate learning and thus improve the dehazing performance. To verify the framework, using the RESIDE data set, experiments were conducted with three types of SDID models, i.e., GCAN, REFN and cGAN. The results show that our method can significantly improve the dehazing performance of the three SDID models. Subjectively, the proposed method generally provides better visual quality. Objectively, our method, using fewer training image pairs, was capable of improving PSNR in the GCAN, REFN, and cGAN models by 3.10 dB, 5.74 dB, and 6.44 dB, respectively. Furthermore, our method was evaluated using a real-world data set, KeDeMa. The results indicate that the better visual quality of the dehazed images is generally for models with the proposed data cleaning scheme. The results demonstrate that the proposed method effectively and efficiently enhances the dehazing performance in the given examples. The practical significance of this research is to provide an easy but effective way, that is, the proposed data cleaning scheme, to improve the performance of SDID models.
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An, Shunmin, Xixia Huang, Linling Wang, Zhangjing Zheng, and Le Wang. "Unsupervised water scene dehazing network using multiple scattering model." PLOS ONE 16, no. 6 (June 28, 2021): e0253214. http://dx.doi.org/10.1371/journal.pone.0253214.
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In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.
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Navale, Miss Anjana, Prof Namdev Sawant, and Prof Umaji Bagal. "Color Attenuation Prior (CAP) for Single Image Dehazing." International Journal Of Engineering And Computer Science 7, no. 02 (February 20, 2018): 23578–84. http://dx.doi.org/10.18535/ijecs/v7i2.10.
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Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we have used a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.
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Hsieh, Cheng-Hsiung, Ze-Yu Chen, and Yi-Hung Chang. "Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm." Sensors 23, no. 2 (January 10, 2023): 815. http://dx.doi.org/10.3390/s23020815.
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Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) method that uses heuristic scaling factors for the model parameters (atmospheric light and initial transmittance). With the IDCP, this paper presents an approach to find optimal scaling factors using the whale optimization algorithm (WOA) and haze level information. The WOA uses ground truth images as a reference in a fitness function to search the optimal scaling factors in the IDCP. The IDCP with the WOA was termed IDCP/WOA. It was observed that the performance of IDCP/WOA was significantly affected by hazy ground truth images. Thus, according to the haze level information, a hazy image discriminator was developed to exclude hazy ground truth images from the dataset used in the IDCP/WOA. To avoid using ground truth images in the application stage, hazy image clustering was presented to group hazy images and their corresponding optimal scaling factors obtained by the IDCP/WOA. Then, the average scaling factors for each haze level were found. The resulting dehazing algorithm was called optimized IDCP (OIDCP). Three datasets commonly used in the image dehazing field, the RESIDE, O-HAZE, and KeDeMa datasets, were used to justify the proposed OIDCP. Then a comparison was made between the OIDCP and five recent haze removal methods. On the RESIDE dataset, the OIDCP achieved a PSNR of 26.23 dB, which was better than IDCP by 0.81 dB, DCP by 8.03 dB, RRO by 5.28, AOD by 5.6 dB, and GCAN by 1.27 dB. On the O-HAZE dataset, the OIDCP had a PSNR of 19.53 dB, which was better than IDCP by 0.06 dB, DCP by 4.39 dB, RRO by 0.97 dB, AOD by 1.41 dB, and GCAN by 0.34 dB. On the KeDeMa dataset, the OIDCP obtained the best overall performance and gave dehazed images with stable visual quality. This suggests that the results of this study may benefit model-based dehazing algorithms.
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Hartanto, Cahyo Adhi, and Laksmita Rahadianti. "Single Image Dehazing Using Deep Learning." JOIV : International Journal on Informatics Visualization 5, no. 1 (March 22, 2021): 76. http://dx.doi.org/10.30630/joiv.5.1.431.
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Many real-world situations such as bad weather may result in hazy environments. Images captured in these hazy conditions will have low image quality due to microparticles in the air. The microparticles light to scatter and absorb, resulting in hazy images with various effects. In recent years, image dehazing has been researched in depth to handle images captured in these conditions. Various methods were developed, from traditional methods to deep learning methods. Traditional methods focus more on the use of statistical prior. These statistical prior have weaknesses in certain conditions. This paper proposes a novel architecture based on PDR-Net by using a pyramid dilated convolution and pre-processing modules, processing modules, post-processing modules, and attention applications. The proposed network is trained to minimize L1 loss and perceptual loss with the O-Haze dataset. To evaluate our architecture's result, we used structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and color difference as an objective assessment and psychovisual experiment as a subjective assessment. Our architecture obtained better results than the previous method using the O-Haze dataset with an SSIM of 0.798, a PSNR of 25.39, but not better on the color difference. The SSIM and PSNR results were strengthened by using subjective assessments and 65 respondents, most of whom chose the results of the restoration of the image produced by our architecture.
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Vishwakarma, Sandeep, Anuradha Pillai, and Deepika Punj. "An Enhancement in Single-Image Dehazing Employing Contrastive Attention over Variational Auto-Encoder (CA-VAE) Method." International Journal of Mathematical, Engineering and Management Sciences 8, no. 4 (August 1, 2023): 728–54. http://dx.doi.org/10.33889/ijmems.2023.8.4.042.
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Hazy images and videos have low contrast and poor visibility. Fog, ice fog, steam fog, smoke, volcanic ash, dust, and snow are all terrible conditions for capturing images and worsening color and contrast. Computer vision applications often fail due to image degradation. Hazy images and videos with skewed color contrasts and low visibility affect photometric analysis, object identification, and target tracking. Computer programs can classify and comprehend images using image haze reduction algorithms. Image dehazing now uses deep learning approaches. The observed negative correlation between depth and the difference between the hazy image’s maximum and lowest color channels inspired the suggested study. Using a contrasting attention mechanism spanning sub-pixels and blocks, we offer a unique attention method to create high-quality, haze-free pictures. The L*a*b* color model has been proposed as an effective color space for dehazing images. A variational auto-encoder-based dehazing network may also be utilized for training since it compresses and attempts to reconstruct input images. Estimating hundreds of image-impacting characteristics may be necessary. In a variational auto-encoder, fuzzy input images are directly given a Gaussian probability distribution, and the variational auto-encoder estimates the distribution parameters. A quantitative and qualitative study of the RESIDE dataset will show the suggested method's accuracy and resilience. RESIDE’s subsets of synthetic and real-world single-image dehazing examples are utilized for training and assessment. Enhance the structural similarity index measure (SSIM) and peak signal-to-noise ratio metrics (PSNR).
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Feng, Mengyao, Teng Yu, Mingtao Jing, and Guowei Yang. "Learning a Convolutional Autoencoder for Nighttime Image Dehazing." Information 11, no. 9 (August 31, 2020): 424. http://dx.doi.org/10.3390/info11090424.
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Currently, haze removal of images captured at night for foggy scenes rely on the traditional, prior-based methods, but these methods are frequently ineffective at dealing with night hazy images. In addition, the light sources at night are complicated and there is a problem of inconsistent brightness. This makes the estimation of the transmission map complicated in the night scene. Based on the above analysis, we propose an autoencoder method to solve the problem of overestimation or underestimation of transmission captured by the traditional, prior-based methods. For nighttime hazy images, we first remove the color effect of the haze image with an edge-preserving maximum reflectance prior (MRP) method. Then, the hazy image without color influence is input into the self-encoder network with skip connections to obtain the transmission map. Moreover, instead of using the local maximum method, we estimate the ambient illumination through a guiding image filtering. In order to highlight the effectiveness of our experiments, a large number of comparison experiments were conducted between our method and the state-of-the-art methods. The results show that our method can effectively suppress the halo effect and reduce the effectiveness of glow. In the experimental part, we calculate that the average Peak Signal to Noise Ratio (PSNR) is 21.0968 and the average Structural Similarity (SSIM) is 0.6802.
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Shi, Zhenghao, Meimei Zhu, Zheng Xia, and Minghua Zhao. "Fast Single-Image Dehazing Method Based on Luminance Dark Prior." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (January 12, 2017): 1754003. http://dx.doi.org/10.1142/s0218001417540039.
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Images captured in hazy weather are usually of poor quality, which has a negative effect on the performance of outdoor computer imaging systems. Therefore, haze removal is critical for outdoor imaging applications. In this paper, a quick single-image dehazing method based on a new effective image prior, luminance dark prior, was proposed. This new image prior arose from the observation that most local patches in the luminance image of a haze-free outdoor YUV color space image usually contain pixels of very low intensity, which is similar to the dark channel prior used with HE for RGB images. Using this new prior, a transmission map was used to estimate the thickness of the haze in an image directly from the luminance component of the YUV color image. To obtain a transmission map with a clear edge outline and depth layer of scene objects, a joint filter containing a bilateral filter and Laplacian operator was employed. Experimental results demonstrated that the proposed method unveiled details and recovered vivid colors even in heavily hazy regions, and provided superior visual effects to many other existing methods.
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Shaheen, Naazaan, and Yogendra Singh. "Improvement of Low-Light Images without Loss of Naturalness based on the Retinex Theory." Journal of Electronic Design Engineering 8, no. 3 (September 3, 2022): 1–11. http://dx.doi.org/10.46610/joede.2022.v08i03.001.
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In modern era, images enhancement is paying very significant role in image analysis and synthesis. We have used the Retinex theory to remove the dark from the first image help improve the clarity of dim or hazy photos. After that, the picture haze must be eliminated, first inverted the image and applied the optimized de-haze on it. By image fusion of both the obtained images through Principal Component Analysis (PCA), a better-quality image was obtained from what we can see in the simulations, things have definitely gotten better.
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Madadikhaljan, M., R. Bahmanyar, S. M. Azimi, P. Reinartz, and U. Sörgel. "SINGLE-IMAGE DEHAZING ON AERIAL IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 687–92. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-687-2019.
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Abstract. Haze contains floating particles in the air which can result in image quality degradation and visibility reduction in airborne data. Haze removal task has several applications in image enhancement and can improve the performance of automatic image analysis systems, namely object detection and segmentation. Unlike rich haze removal literature in ground imagery, there is a lack of methods specifically designed for aerial imagery, considering the fact that there is a characteristic difference between the aerial imagery domain and ground one. In this paper, we propose a method to dehaze aerial images using Convolutional Neural Networks (CNNs). Currently, there is no available data for dehazing methods in aerial imagery. To address this issue, we have created a syntheticallyhazed aerial image dataset to train the neural network on aerial hazy image dataset. We train All-in-One dehazing network (AODNet) as the base approach on hazy aerial images and compare the performance of our proposed approach against the classical model. We have tested our model on natural as well as the synthetically-hazed aerial images. Both qualitative and quantitative results of the adapted network show an improvement in dehazing results. We show that the adapted AOD-Net on our aerial image test set increases PSNR and SSim by 2.2% and 9%, respectively.
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Filin, A., A. Kopylov, and I. Gracheva. "A SINGLE IMAGE DEHAZING DATASET WITH LOW-LIGHT REAL-WORLD INDOOR IMAGES, DEPTH MAPS AND INFRARED IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 53–57. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-53-2023.
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Abstract. Benchmarking of haze removal methods and training related models requires appropriate datasets. The most objective metrics of assessment quality of dehazing are shown by reference metrics – i.e. those in which the reconstructed image is compared with the reference (ground-truth) image without haze. The dehazing datasets consist of pairs where haze is artificially synthesized on ground-truth images are not well suited for the assessment of the quality of dehazing methods. Accommodation of the real-world environment for take truthful pairs of hazy and haze-free images are difficult, so there are few image dehazing datasets, which consists with the real both hazy and haze-free images. The currently researcher’s attention is shifting to dehazing on “more complex” images, including those that are obtained in insufficient illumination conditions and with the presence of localized light sources. It is almost no datasets with such pairs of images, which makes it difficult of objective assessment of image dehazing methods. In this paper, we present extended version of our previously proposed dataset of this kind with more haze density levels and depths of scenes. It consists of images of 2 scenes at 4 lighting and 8 haze density levels – 64 frames in total. In addition to images in the visible spectrum, for each frame depth map and thermal image was captured. An experimental evaluation of state-of-the art haze removal methods was carried out on the resulting dataset. The dataset is available for free download at https://data.mendeley.com/datasets/jjpcj7fy6t.
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Hari, U., and A. Ruhan Bevi. "A novel technique for spatiotemporal dahazing of video image." Journal of Physics: Conference Series 2335, no. 1 (September 1, 2022): 012055. http://dx.doi.org/10.1088/1742-6596/2335/1/012055.
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Abstract Recently video surveillance in smart city projects is becoming more and more popular. Generally, high quality image is required in video image analysis and recognition. Often bad weather conditions like atmospheric haze, fog, and smoke affect captured outdoor images and result in loss of visibility and poor contrast. In this paper, we propose a new method for a single image and video dehazing. Many complex methods are existing for removing haze from hazy images. In this paper, we propose a method that combines dark channel prior(DCP) and bright channel prior(BCP) along with a guided filtering technique to perform effectively and efficiently by spatiotemporal means in video dehazing. To extract the global atmospheric light accurately, we exploit multiple prior DCP and BCP underlying hazy images. In addition, the rough transmission map is estimated and improvised using a guided filter to get refined transmission. The experimental result shows that our proposed algorithm enhances the colour fidelity reduces the halo effect and improves the efficiency of video dehazing.
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Zhao, Wenxuan, Yaqin Zhao, Liqi Feng, and Jiaxi Tang. "Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze." Electronics 10, no. 22 (November 22, 2021): 2868. http://dx.doi.org/10.3390/electronics10222868.
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The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.
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Xu, Linli, Jing Han, Tian Wang, and Lianfa Bai. "Global Image Dehazing via Frequency Perception Filtering." Journal of Circuits, Systems and Computers 28, no. 09 (August 2019): 1950142. http://dx.doi.org/10.1142/s0218126619501421.
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In outdoor scenes, haze limits the visibility of images, and degrades people’s judgement of the objects. In this paper, based on an assumption of human visual perception in frequency domain, a novel image haze removal filtering is proposed. Combining this assumption with the theory of frequency domain filtering, we first estimate the cut-off frequency to divide the frequency domain of the hazy image into three components — low-frequency domain, intermediate-frequency domain and high-frequency domain. Then, by introducing the weighting factors, the three components are recombined together. After the theoretical deduction of frequency domain, the establishment of the actual model and adjusting the cut-off frequency and weighting factors, we finally acquire a global and adaptive filtering. This filtering can restore the details and the contours of the images, which have less noise, and improve the visibility of the objects in hazy images. Moreover, our method is simple in structure and strongly applicable, and rarely affected by parameters. Our algorithm is stable and performs well in heavy fog and the scene changes.
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Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, Yong Li, Guanqiu Qi, Neal Mazur, Yuanyuan Li, and Penglong Li. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (June 22, 2021): 2432. http://dx.doi.org/10.3390/rs13132432.
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Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.
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Fan, Yunsheng, Longhui Niu, and Ting Liu. "Multi-Branch Gated Fusion Network: A Method That Provides Higher-Quality Images for the USV Perception System in Maritime Hazy Condition." Journal of Marine Science and Engineering 10, no. 12 (December 1, 2022): 1839. http://dx.doi.org/10.3390/jmse10121839.
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Image data acquired by unmanned surface vehicle (USV) perception systems in hazy situations is characterized by low resolution and low contrast, which can seriously affect subsequent high-level vision tasks. To obtain high-definition images under maritime hazy conditions, an end-to-end multi-branch gated fusion network (MGFNet) is proposed. Firstly, residual channel attention, residual pixel attention, and residual spatial attention modules are applied in different branch networks. These attention modules are used to focus on high-frequency image details, thick haze area information, and contrast enhancement, respectively. In addition, the gated fusion subnetworks are proposed to output the importance weight map corresponding to each branch, and the feature maps of three different branches are linearly fused with the importance weight map to help obtain the haze-free image. Then, the network structure is evaluated based on the comparison with pertinent state-of-the-art methods using artificial and actual datasets. The experimental results demonstrate that the proposed network is superior to other previous state-of-the-art methods in the PSNR and SSIM and has a better visual effect in qualitative image comparison. Finally, the network is further applied to the hazy sea–skyline detection task, and advanced results are still achieved.
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Song, Yingchao, Haibo Luo, Junkai Ma, Bin Hui, and Zheng Chang. "Sky Detection in Hazy Image." Sensors 18, no. 4 (April 1, 2018): 1060. http://dx.doi.org/10.3390/s18041060.
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Zheng, Shunyuan, Jiamin Sun, Qinglin Liu, Yuankai Qi, and Jianen Yan. "Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network." Electronics 9, no. 11 (November 8, 2020): 1877. http://dx.doi.org/10.3390/electronics9111877.
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In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land-scene images and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose a Generative Adversial Network (GAN)-based method called OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset contains unpaired hazy and clean images taken over water. The proposed OWI-DehazeGAN is composed of an encoder–decoder framework, supervised by a forward-backward translation consistency loss for self-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment neural network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods. Compared with the state-of-the-art, our method gains a significant improvement by 1.94% for SSIM, 7.13% for PSNR and 4.00% for CIEDE2000 on the synthetic test dataset.
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Yousaf, Rehan Mehmood, Hafiz Adnan Habib, Zahid Mehmood, Ameen Banjar, Riad Alharbey, and Omar Aboulola. "Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability-Weighted Moments." Mathematical Problems in Engineering 2019 (December 2, 2019): 1–11. http://dx.doi.org/10.1155/2019/9721503.
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The method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments. Different factors make the image dehazing process cumbersome like unbalanced airlight, contrast, and darkness in hazy images. Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges. The proposed technique can preserve better edges and illumination and retain the original color of the image. Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. PWM is very effective as it suppresses low variations present in images that are affected by the haze. We have proposed a method in this article that performs well as compared to state-of-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and preserving edges without producing halo effects within the image. The qualitative and quantitative analysis carried on standard image databases proves its robustness in terms of the standard performance evaluation metrics.
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Rao, K. Srinivasa, S. Ramya, N. Ramyasri, and M. Sirisha. "A Dehazing Benchmark with Real Hazy Outdoor Images." International Journal of Advance Research and Innovation 8, no. 2 (2020): 59–63. http://dx.doi.org/10.51976/ijari.822010.
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A single image dehazing is one of the problemin image processing. The main aim of using this method is to obtain certain transmission map to abolish hazes from a single input image. An optical model is evaluated and the basic transmission map under an additional filter is modified. For better conservation of haze image, the globally guided image filtering can be applied to produce sharper images and preserves details in regions of fine structure visibly.
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KAPLAN, Nur Hüseyin. "Single Image Dehazing based on Additive Wavelet Transform." Balkan Journal of Electrical and Computer Engineering 11, no. 1 (January 30, 2023): 71–77. http://dx.doi.org/10.17694/bajece.1127633.
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In this work, a single image dehazing method, which uses the multiscale products of additive wavelet transform as a prior is presented. In this method, first, the additive wavelet transform is applied to the hazy image to obtain its approximation and wavelet layers. Then the multiscale products of the approximation and detail layers of the input hazy image is calculated. The multiscale products of approximation and wavelet layers are summed up to obtain the proposed prior. Observations demonstrate that the proposed prior calculation keeps the detail information of the image, while detecting the haze. Using the proposed prior and commonly used hazy image model together, an efficient dehazing method is constructed. The comparisons between the proposed method and commonly used dehazing methods show that the proposed method has a better dehazing perfomrance than the traditional methods.
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Sarker, Aditi, Morium Akter, and Mohammad Shorif Uddin. "Simulation of Hazy Image and Validation of Haze Removal Technique." Journal of Computer and Communications 07, no. 02 (2019): 62–72. http://dx.doi.org/10.4236/jcc.2019.72005.
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Lee, Ho Sang. "Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior." Technologies 9, no. 4 (December 17, 2021): 101. http://dx.doi.org/10.3390/technologies9040101.
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A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images.
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Sun, Zaiming, Chang’an Liu, Hongquan Qu, and Guangda Xie. "A Novel Effective Vehicle Detection Method Based on Swin Transformer in Hazy Scenes." Mathematics 10, no. 13 (June 23, 2022): 2199. http://dx.doi.org/10.3390/math10132199.
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Under bad weather, the ability of intelligent vehicles to perceive the environment accurately is an important research content in many practical applications such as smart cities and unmanned driving. In order to improve vehicle environment perception technology in real hazy scenes, we propose an effective detection algorithm based on Swin Transformer for hazy vehicle detection. This algorithm includes two aspects. First of all, for the aspect of the difficulty in extracting haze features with poor visibility, a dehazing network is designed to obtain high-quality haze-free output through encoding and decoding methods using Swin Transformer blocks. In addition, for the aspect of the difficulty of vehicle detection in hazy images, a new end-to-end vehicle detection model in hazy days is constructed by fusing the dehazing module and the Swin Transformer detection module. In the training stage, the self-made dataset Haze-Car is used, and the haze detection model parameters are initialized by using the dehazing model and Swin-T through transfer learning. Finally, the final haze detection model is obtained by fine tuning. Through the joint learning of dehazing and object detection and comparative experiments on the self-made real hazy image dataset, it can be seen that the detection performance of the model in real-world scenes is improved by 12.5%.
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Kaplan, N. H. "Remote sensing image enhancement using hazy image model." Optik 155 (February 2018): 139–48. http://dx.doi.org/10.1016/j.ijleo.2017.10.132.
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Song, Runze, Zhaohui Liu, and Chao Wang. "End-to-end dehazing of traffic sign images using reformulated atmospheric scattering model." Journal of Intelligent & Fuzzy Systems 41, no. 6 (December 16, 2021): 6815–30. http://dx.doi.org/10.3233/jifs-210733.
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As an advanced machine vision task, traffic sign recognition is of great significance to the safe driving of autonomous vehicles. Haze has seriously affected the performance of traffic sign recognition. This paper proposes a dehazing network, including multi-scale residual blocks, which significantly affects the recognition of traffic signs in hazy weather. First, we introduce the idea of residual learning, design the end-to-end multi-scale feature information fusion method. Secondly, the study used subjective visual effects and objective evaluation metrics such as Visibility Index (VI) and Realness Index (RI) based on the characteristics of the real-world environment to compare various traditional dehazing and deep learning dehazing method with good performance. Finally, this paper combines image dehazing and traffic sign recognition, using the algorithm of this paper to dehaze the traffic sign images under real-world hazy weather. The experiments show that the algorithm in this paper can improve the performance of traffic sign recognition in hazy weather and fulfil the requirements of real-time image processing. It also proves the effectiveness of the reformulated atmospheric scattering model for the dehazing of traffic sign images.
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Ngo, Dat, Gi-Dong Lee, and Bongsoon Kang. "A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps." Applied Sciences 9, no. 17 (August 21, 2019): 3443. http://dx.doi.org/10.3390/app9173443.
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This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter using the well-known Batcher’s parallel sort. Hardware verification confirmed that high-resolution video standards were processed in real time for haze removal.
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Zhao, Ruhao, Xiaoping Ma, He Zhang, Honghui Dong, Yong Qin, and Limin Jia. "Enhanced densely dehazing network for single image haze removal under railway scenes." Smart and Resilient Transport 3, no. 3 (October 18, 2021): 218–34. http://dx.doi.org/10.1108/srt-12-2020-0029.
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Purpose This paper aims to propose an enhanced densely dehazing network to suit railway scenes’ features and improve the visual quality degraded by haze and fog. Design/methodology/approach It is an end-to-end network based on DenseNet. The authors design enhanced dense blocks and fuse them in a pyramid pooling module for visual data’s local and global features. Multiple ablation studies have been conducted to show the effects of each module proposed in this paper. Findings The authors have compared dehazed results on real hazy images and railway hazy images of state-of-the-art dehazing networks with the dehazed results in data quality. Finally, an object-detection test is taken to judge the edge information preservation after haze removal. All results demonstrate that the proposed dehazing network performs better under railway scenes in detail. Originality/value This study provides a new method for image enhancing in the railway monitoring system.
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Bibi,, N. Ameena, and Dr C. Vasanthanayaki. "Study on Haze Imaging Model and Image Restoration Schemes for Hazy Remote Sensing Images." International Journal of Research in Advent Technology 7, no. 6 (July 10, 2019): 61–69. http://dx.doi.org/10.32622/ijrat.76201940.
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Martínez-Domingo, Miguel Ángel, Eva M. Valero, Juan L. Nieves, Pedro Jesús Molina-Fuentes, Javier Romero, and Javier Hernández-Andrés. "Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range." Sensors 20, no. 22 (November 23, 2020): 6690. http://dx.doi.org/10.3390/s20226690.
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In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have tested and applied five single image dehazing algorithms, originally developed to work on RGB images and not requiring user interaction and/or prior knowledge about the images, on a spectral hazy image database in the visible range. We have made the evaluation using two strategies: the first is based on the analysis of eleven state-of-the-art metrics and the second is two psychophysical experiments with 126 subjects. Our results suggest that the higher the wavelength within the visible range is, the higher the quality of the dehazed images. The quality increases for low haze/fog levels. The choice of the best performing algorithm depends on the criterion prioritized by the metric design strategy. The psychophysical experiment results show that the level of agreement between observers and metrics depends on the criterion set for the observers’ task.
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Wang, Wei, Wenhui Li, Qingji Guan, and Miao Qi. "Multiscale Single Image Dehazing Based on Adaptive Wavelet Fusion." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/131082.
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Removing the haze effects on images or videos is a challenging and meaningful task for image processing and computer vision applications. In this paper, we propose a multiscale fusion method to remove the haze from a single image. Based on the existing dark channel prior and optics theory, two atmospheric veils with different scales are first derived from the hazy image. Then, a novel and adaptive local similarity-based wavelet fusion method is proposed for preserving the significant scene depth property and avoiding blocky artifacts. Finally, the clear haze-free image is restored by solving the atmospheric scattering model. Experimental results demonstrate that the proposed method can yield comparative or even better results than several state-of-the-art methods by subjective and objective evaluations.
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He, Renjie, Xintao Guo, and Zhongke Shi. "SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images." Sensors 20, no. 18 (September 16, 2020): 5300. http://dx.doi.org/10.3390/s20185300.
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Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years, they are mainly designed for haze removal in daytime. In nighttime, dehazing is more challenging where most daytime dehazing methods become invalid due to multiple scattering phenomena, and non-uniformly distributed dim ambient illumination. While a few approaches have been proposed for nighttime image dehazing, low ambient light is actually ignored. In this paper, we propose a novel unified nighttime hazy image enhancement framework to address the problems of both haze removal and illumination enhancement simultaneously. Specifically, both halo artifacts caused by multiple scattering and non-uniformly distributed ambient illumination existing in low-light hazy conditions are considered for the first time in our approach. More importantly, most current daytime dehazing methods can be effectively incorporated into nighttime dehazing task based on our framework. Firstly, we decompose the observed hazy image into a halo layer and a scene layer to remove the influence of multiple scattering. After that, we estimate the spatially varying ambient illumination based on the Retinex theory. We then employ the classic daytime dehazing methods to recover the scene radiance. Finally, we generate the dehazing result by combining the adjusted ambient illumination and the scene radiance. Compared with various daytime dehazing methods and the state-of-the-art nighttime dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate the superiority of our framework in terms of halo mitigation, visibility improvement and color preservation.
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Gu, Zhenfei, Mingye Ju, and Dengyin Zhang. "A Single Image Dehazing Method Using Average Saturation Prior." Mathematical Problems in Engineering 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/6851301.
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Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness.
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Liu, Feilu. "An overview of image enhancement dehazing algorithms." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 738–42. http://dx.doi.org/10.54254/2755-2721/4/2023411.
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This dissertation is an overview of Image dehazing algorithm that is utilized to process hazy images through certain technologies to remove the image haze occlusion and interference, improve the visual effect of image. For example, the contrast, color and detail and other aspects. The research method is literature review. The image enhancement algorithms mainly include histogram equilibrium, homomorphic filtering, wavelet transformation and Retinex method. The of these algorithms will be discussed detailly in the following sections of the article. The conclusion is that due to the error of the parameter information in the image with fog, the current defogging algorithm is still unable to achieve perfect results.
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Wang, Shuhang, Yu Tian, Tian Pu, Patrick Wang, and Petra Perner. "A Hazy Image Database with Analysis of the Frequency Magnitude." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 05 (January 3, 2018): 1854012. http://dx.doi.org/10.1142/s0218001418540125.
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Image haze removal has been extensively studied, but there has been no such an image database regarding the haze level. It is not convenient for readers to verify the assumptions or priors that are supposed to be useful for haze removal, and meanwhile, it is not fair to compare the performance of haze removal methods, which are effective for images with different haze levels. To solve this problem, we built a database consisting of more than 3464 images of different kinds of outdoor scenes. The images of the database are grouped into four classes regarding the haze level. Along with the database, we also observe a frequency magnitude prior, i.e. the frequency magnitude decreases with the increasing haze level, which can be used as a prior to develop haze removal methods. Our purpose is to help develop image haze removal methods, as well as verify existing statistical priors and discover new ones that can be used for image processing.
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Borkar, Samarth, and Sanjiv V. Bonde. "A Fusion Based Visibility Enhancement of Single Underwater Hazy Image." International Journal of Advances in Applied Sciences 7, no. 1 (March 1, 2018): 38. http://dx.doi.org/10.11591/ijaas.v7.i1.pp38-45.
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<span lang="EN-IN">Underwater images are prone to contrast loss, limited visibility, and undesirable color cast. For underwater computer vision and pattern recognition algorithms, these images need to be pre-processed. We have addressed a novel solution to this problem by proposing fully automated underwater image dehazing using multimodal DWT fusion. Inputs for the combinational image fusion scheme are derived from Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for contrast enhancement in HSV color space and color constancy using Shades of Gray algorithm respectively. To appraise the work conducted, the visual and quantitative analysis is performed. The restored images demonstrate improved contrast and effective enhancement in overall image quality and visibility. The proposed algorithm performs on par with the recent underwater dehazing techniques.</span>
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Lee, Ho Sang. "Eximious Sandstorm Image Improvement Using Image Adaptive Ratio and Brightness-Adaptive Dark Channel Prior." Symmetry 14, no. 7 (June 28, 2022): 1334. http://dx.doi.org/10.3390/sym14071334.
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Sandstorm images have a color cast by sand particles. Hazy images have similar features to sandstorm images due to these images having a common obtaining process. To improve hazy images, various dehazing methods are being studied. However, not all methods are appropriate for enhancing sandstorm images as they experience color degradation via an imbalanced color channel and degraded color distributed around the image. Therefore, this paper proposes two steps to improve sandstorm images. The first is a color-balancing step using the mean ratio of the color channel between red and other colors. The sandstorm image has a degraded color channel, and therefore, the attenuated color channel has different average values for each color channel; the red channel’s average value is the highest, and that of the blue channel is the lowest. Using this property, this paper balances the color of images via the ratio of color channels. Although the image is enhanced, if the red channel is still the most abundant, the enhanced image may have a reddish color. Therefore, to enhance the image naturally, the red channel is adjusted by the average ratio of the color channel; those measures (as with the average ratio of color channels) are called image adaptive ratio (IAR). Because color-balanced sandstorm images have the same characteristics as hazy images, to enhance them, a dehazing method is applied. Ordinary dehazing methods often use dark channel prior (DCP). Though DCP estimates the dark region of an image, because the intensity of brightness is too high, the estimated DCP is not sufficiently dark. Additionally, DCP is able to show the artificial color shift in the enhanced image. To compensate for this point, this paper proposes a brightness-adaptive dark channel prior (BADCP) using a normalized color channel. The image improved using the proposed method has no color distortion or artificial color. The experimental results show the superior performance of the proposed method in comparison with state-of-the-art dehazing methods, both subjectively and objectively.
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Zhan, Yun, Sheng Chun Zheng, Yong Du, and Lu Qing Wei. "Automated Haze Removal and Radiometric Normalization for Electro-Optical Imagery Preprocessing." Advanced Engineering Forum 6-7 (September 2012): 391–97. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.391.
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Two precedures are presented for image preprocessing, automated haze removal and the relative radiometric normalization of multitemporal optical images, to act as the data support for land cover change detection and image analysis. The developed algorithm for haze removal involves processes of feature and texture analysis of the multiresolution spatial frequency distribution of pixel brightness information content of a scene. The image contaminated by haze is decomposed into layers of different resolutions of spatial distribution frequencies. The radiometric characteristics of the corresponding layers are estimated and analyzed with topology based multiresolution spatial analysis technology. Based on the analysis, the haze component is then separated from the remaining spatial frequency components representing spectral information of actual land cover types in the scene, and a spectrally corrected image with “haze-off” characteristics is obtained. Then a method is used for radiometric normalization between multitemporal images of the same area. Case study using several different type images of Qingdao City in China proves the effectiveness of this technique except for those regions too hazy.
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Hu, Xianjun, Jing Wang, and Guilian Li. "Contrastive Learning-Based Haze Visibility Enhancement in Intelligent Maritime Transportation System." Journal of Advanced Transportation 2022 (September 30, 2022): 1–19. http://dx.doi.org/10.1155/2022/2160044.
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With the rapid development of artificial intelligence and big traffic data, the data-driven intelligent maritime transportation has received significant attention in both industry and academia. It is capable of improving traffic efficiency and reducing traffic accidents in maritime applications. However, video cameras often suffer from severe haze weather, leading to degraded visual data and ineffective maritime surveillance. It is thus necessary to restore the visually degraded images and to guarantee maritime transportation efficiency and safety under hazy imaging conditions. In this work, a contrastive learning framework is proposed for haze visibility enhancement in intelligent maritime transportation systems. In particular, the proposed learning method could fully learn both local and global image features, which are beneficial for visual quality improvement. A total of 100 clean images containing water traffic scenes were selected as the synthetic test dataset, and good dehazing results were achieved on both visual and indexing results (e.g., peak signal to noise ratio (PSNR): 23.95 ± 3.48 and structural similarity index (SSIM): 0.924 ± 0.065 for different transmittance and atmospheric light values). In addition, extensive experiments on real-world 100 water hazy images demonstrate the effectiveness of the proposed method (e.g., natural image quality evaluator (NIQE): 4.800 ± 0.634 and perception-based image quality evaluator (PIQE): 46.320 ± 10.253 ). The enhanced images could be effectively exploited for promoting the accuracy and robustness of ship detection. The maritime traffic supervision and management could be accordingly improved in the intelligent transportation system.
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Min, Xiongkuo, Guangtao Zhai, Ke Gu, Yucheng Zhu, Jiantao Zhou, Guodong Guo, Xiaokang Yang, Xinping Guan, and Wenjun Zhang. "Quality Evaluation of Image Dehazing Methods Using Synthetic Hazy Images." IEEE Transactions on Multimedia 21, no. 9 (September 2019): 2319–33. http://dx.doi.org/10.1109/tmm.2019.2902097.
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