Journal articles: 'Image restoration'

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Chan, A., and J. Meloche. "Image restoration." Journal of Statistical Planning and Inference 65, no. 2 (December 1997): 233–54. http://dx.doi.org/10.1016/s0378-3758(97)00066-9.

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Qiu, Peihua. "Image restoration." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 1 (July 2009): 110–13. http://dx.doi.org/10.1002/wics.7.

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Gupta, Nakul Kumar, and Dr S. K. Manju Bargavi. "Image Restoration." International Journal of Innovative Research in Information Security 10, no. 02 (February 10, 2024): 42–50. http://dx.doi.org/10.26562/ijiris.2023.v1002.01.

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Image restoration is an integral component of computer vision that tries to restore pictures that have been deteriorated or corrupted to their original or enhanced condition. In this study, we look into the wide-ranging terrain of picture restoration techniques, which includes both conventional filter-based approaches and cutting-edge deep learning models. There are certain circumstances in which traditional approaches, such as Wiener filtering and bilateral filtering, perform quite well, particularly when it comes to smoothing and noise reduction. On the other hand, the fact that they rely on handcrafted filters restricts their adaptation to more complicated forms of degradation. Visual restoration has been revolutionized by deep learning, which is led by convolutional neural networks (CNNs). Deep learning involves learning sophisticated representations of visual data. It is because of this that CNNs are able to deal with a wide variety of degradations, such as noise, blurring, artifacts, and missing data. Generative adversarial networks, often known as GANs, are continually pushing the limits of what is possible by utilizing adversarial training to accomplish spectacular outcomes in the areas of in painting and picture super-resolution. Despite amazing development, there are still obstacles to overcome: Understanding the inner workings of deep learning models continues to be a challenge, thanks to the limited interpretability of the data. Dependence on data: Acquiring large quantities of high-quality data is necessary for the training of successful models. Costs associated with computation: The process of training and deploying deep learning models may be quite computationally rigorous. The improvement of camera vision for autonomous cars in order to make navigation safer and more dependable overall. Image restoration technology has the potential to continue to revolutionize image processing and analysis, ultimately contributing to advancements across a wide range of scientific and technological domains. This can be accomplished by addressing the challenges that are currently being faced and concentrating on the promising research directions that are currently being pursued.

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Shah, Ankur N., and Dr K. H. Wandra Dr. K. H. Wandra. "Introduction to noise, image restoration and comparison of various methods of image restoration by removing noise from image." Indian Journal of Applied Research 2, no. 1 (October 1, 2011): 67–68. http://dx.doi.org/10.15373/2249555x/oct2012/22.

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Rathee, S., Z. J. Koles, and T. R. Overton. "Image restoration in computed tomography: restoration of experimental CT images." IEEE Transactions on Medical Imaging 11, no. 4 (1992): 546–53. http://dx.doi.org/10.1109/42.192690.

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Wu, Xue Feng, and Yu Fan. "A Research on the Optimization of Fuzzy Image." Applied Mechanics and Materials 409-410 (September 2013): 1593–96. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1593.

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The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.

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Chauhan, Vimal. "Reduction of Noise in Restoration of Images Using Mean and Median Filtering Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 301–13. http://dx.doi.org/10.22214/ijraset.2021.37965.

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Abstract: The purpose of this paper is to present a study of digital technology approaches to image restoration. This process of image restoration is crucial in many areas such as satellite imaging, astronomical image & medical imaging where degraded images need to be repaired Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms [2]. Image restoration can be described as an important part of image processing technique. Image restoration has proved to be an active field of research in the present days. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing [2]. In this paper, an image restoration algorithm based on the mean and median calculation of a pixel has been implemented. We focused on a certain iterative process to carry out restoration. The algorithm has been tested on different images with different percentage of salt and pepper noise. The improved PSNR and MSE values has been obtained. Keywords: De-Noising, Image Filtering, Mean Filter & Median Filter, Salt and Pepper Noise, Denoising Techniques, Image Restoration.

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Wieslander, Håkan, Carolina Wählby, and Ida-Maria Sintorn. "TEM image restoration from fast image streams." PLOS ONE 16, no. 2 (February 1, 2021): e0246336. http://dx.doi.org/10.1371/journal.pone.0246336.

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Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are degraded by both motion blur and noise. Building on deep learning based approaches developed for deblurring videos of natural scenes we explore the opportunities and limitations of deblurring and denoising images captured from a fast image stream collected by a TEM microscope. We start from existing neural network architectures and make adjustments of convolution blocks and loss functions to better fit TEM data. We present deblurring results on two real datasets of images of kidney tissue and a calibration grid. Both datasets consist of low quality images from a fast image stream captured by moving the sample under the microscope, and the corresponding high quality images of the same region, captured after stopping the movement at each position to let all motion settle. We also explore the generalizability and overfitting on real and synthetically generated data. The quality of the restored images, evaluated both quantitatively and visually, show that using deep learning for image restoration of TEM live image streams has great potential but also comes with some limitations.

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Fan, Yu, and Xue Feng Wu. "Study on Motion Blur Image Restoration Algorithms." Advanced Materials Research 753-755 (August 2013): 2976–79. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2976.

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The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given£®According to the characteristics of blurred images£¬the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration,Lucy-Richardson image restoration and Wiener image restoration.The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved ,and the image restoration is more stable.

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Tahir KASIM, Ghada Mohammad, Zahraa Mazin ALKATTAN, and Nadia Maan MOHAMMED. "Hybrid System for Image Restoration." International Research Journal of Innovations in Engineering and Technology 08, no. 01 (2024): 168–77. http://dx.doi.org/10.47001/irjiet/2024.801020.

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The image processing field is considered one of the highly sensitive fields for accuracy due to the quality of the processing in view of the visual view of the user and due to the development in modern means of communication and the use of these means in the transfer of images and the impact of these means on several factors, including external, including those related to the quality of the source signal and the impact of the transmitted images by these conditions, digital correction processes have emerged to reach a high quality of the received image. Most of the studies and research on digital image correction have focused on the quality and time required for correction processes, and some have focused on using traditional optimization algorithms to obtain acceptable visual quality, while others have focused on shortening time regardless of quality, and due to the fact that all studies and research that have been viewed were focused on the use of speculative methods and hybrid algorithms to address distortion in images, as all weaknesses were related to time, quality and calculations because the size of the image data is large Very. The research aims to study digital images and then process images, optimization methods, genetic algorithms and accomplish an algorithm with high features. In this paper, the simple genetic algorithm is used in the process of correcting images of the type (.JPG), as this method is characterized by the fact that it includes many of the advantages of the previous methods in addition to additional features that provided quality, accuracy and shortening time in calculations. The paper has been completed in five phases: The first stage: Providing external protection for the system by entering the password. Second Stage: Creating the system's database. Third stage: Create (code book) in a new style based on the size of the file used. Fourth stage: Building the genetic algorithm for correction Fifth stage: Using a mathematical model to add distortion to a clear image, correct it and compare the results.

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Muhson, Meryem H., and Ayad A. Al-Ani. "BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK." Iraqi Journal of Information and Communications Technology 1, no. 1 (December 15, 2021): 25–32. http://dx.doi.org/10.31987/ijict.1.1.178.

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Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.

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Tan, Yi, Jin Fan, Dong Sun, Qingwei Gao, and Yixiang Lu. "Multi-scale Image Denoising via a Regularization Method." Journal of Physics: Conference Series 2253, no. 1 (April 1, 2022): 012030. http://dx.doi.org/10.1088/1742-6596/2253/1/012030.

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Abstract Image restoration is a widely studied problem in the field of image processing. Although the existing image restoration methods based on denoising regularization have shown relatively well performance, image restoration methods for different features of unknown images have not been proposed. Since images have different features, it seems necessary to adopt different priori regular terms for different features. In this paper, we propose a multiscale image regularization denoising framework that can simultaneously perform two or more denoising prior regularization terms to better obtain the overall image restoration results. We use the alternating direction multiplier method (ADMM) to optimize the model and combine multiple denoising algorithms for extensive image deblurring and image super-resolution experiments, and our algorithm shows better performance compared to the existing state-of-the-art image restoration methods.

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Jain, Geetanjali, and Supreet Kaur. "An Improved Image Restoration Technique for GIF Images." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 313. http://dx.doi.org/10.23956/ijarcsse.v7i8.78.

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Image restoration is used to emphasize and sharpen image features for display and analysis. Image restoration is the process of applying these techniques to facilitate the development of a solution to a computer imaging problem. Image restoration is an important issue in high level image processing which deals with recovering of an original and sharp image using a degradation and restoration model. During image acquisition process degradation occurs. Image restoration is used to estimate the original image from the degraded data. Aim of this research work is to provide a concise overview of most useful restoration models. Using the proposed approach the features of the neighboring pixels are calculated and on basis of these features image is restored. In this research work we use canny edge detection technique to find edges and use probability recovery method to find distortion in each pixel. Using thresholding value restore the distorted pixels and filter restored image. In the end performance evaluation of proposed method is performed based on various parameters like MSE, PSNR, contrast, and coefficient of correlation.

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Meloche, J., and R. H. Zamar. "Binary-image restoration." Canadian Journal of Statistics 22, no. 3 (September 1994): 335–55. http://dx.doi.org/10.2307/3315596.

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Banham, M. R., and A. K. Katsaggelos. "Digital image restoration." IEEE Signal Processing Magazine 14, no. 2 (March 1997): 24–41. http://dx.doi.org/10.1109/79.581363.

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Rosa, Michael R. "HST Image Restoration." Highlights of Astronomy 9 (1992): 493–95. http://dx.doi.org/10.1017/s1539299600009631.

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Experience gained in a world wide effort to exploit restoration methods in order to improve the scientific return from the degraded HST optics calms down fears that HST might have lost all of its spatial resolving power. However, the problems posed by the characteristics of the point spread function (PSF) and of the detectors are such that “black-box” image restoration in an automatic procedure during pipe-line calibration is not applicable. Results from restoration experiments show the directions for future research: On the practical side ways are required to properly estimate the complicated PSF, to objectively describe errors, to treat undersampling and non-linear detectors, and to gain factors in throughput for large amounts of data; innovative example are methods to combine data obtained with differing PSFs (ground – space, space – space) and sampling.

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Dubonos, S. N., B. N. Gaifullin, and N. G. Ushakov. "Statistical image restoration." Computers & Mathematics with Applications 19, no. 1 (1990): 39–45. http://dx.doi.org/10.1016/0898-1221(90)90080-4.

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Belfkih, S., and P. Montesinos. "Color Image Restoration." Conference on Colour in Graphics, Imaging, and Vision 1, no. 1 (January 1, 2002): 416–19. http://dx.doi.org/10.2352/cgiv.2002.1.1.art00088.

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Wang, Wei, and Yihua Yan. "A New Image Restoration Method for MUSER." Advances in Astronomy 2019 (May 2, 2019): 1–5. http://dx.doi.org/10.1155/2019/8087405.

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Solar radio images in decimeter wave range consist of many complicated components including a disk component, some bright and weak compact sources, and many diffuse features. Complicated structures combining these various components maybe cause restoration failure when using conventional algorithms. Furthermore, the images at different frequencies band are pretty different. Therefore, restoration method for solar radio image is different from other radio sources. Some image restoration methods were applied and obtained good results on Nancay radioheliograph images and Nobeyama radioheliograph images, and some new methods were introduced into processing these complicated solar radio images in recent years. For a new radioheliograph with ultrawide frequency band, new image restoration method which can maximize function of telescope is demanded. Different images could be obtained from the same visibilities data by using different weighting functions in imaging processing. In this paper, a new restoration method for solar radio image was proposed. Two images with different weighting functions from the same data are combined in this method. This restoration method has applied to data processing of Mingantu spectral radioheliograph.

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Wu, Xue Feng, and Yu Fan. "A Research for Fuzzy Image Restoration." Advanced Materials Research 955-959 (June 2014): 1085–88. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.1085.

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Computational photography and image processing technology are used to restore the clearness of images taken in fog scenes autmatically.The technology is used to restore the clearness of the fog scene,which includes digital image processing and the physical model of atmospheric scattering.An algorithm is designed to restore the clearness of the fog scene under the assumption of the albedo images and then the resolution algorithm is analysised.The algorithm is implemented by the software of image process ,which can improve the efficiency of the algorithm and interface.The fog image and defogging image are compared, and the results show that the visibility of the image is improved, and the image restoration is more clearly.

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Zhan-Peng Cui, Zhan-Peng Cui. "Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network." 電腦學刊 34, no. 4 (August 2023): 001–14. http://dx.doi.org/10.53106/199115992023083404001.

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<p>In contrast enhancement of fuzzy defect image, details loss and noise expansion are east to occur, which brings difficulties to subsequent image analysis and defect recognition. Therefore, a fuzzy defect image restoration and enhancement method based on neural network is proposed. A double fusion neural network composed of a depth generation network and a discrimination network is designed. The residual of the denoised fuzzy image and the real image is output by the network, which is input into the discrimination network together with the real image, and the difference between the two is judged by the total loss function. To solve the problem of pixel coordinate value of fuzzy defect image, neural network is used to build a fast correction algorithm. Therefore, a fuzzy image restoration and enhancement method based on neural network is proposed to improve the image quality. By reconstructing the resolution of fuzzy defect image, a hierarchical enhancement method of fuzzy defect image region is constructed to achieve fuzzy defect image restoration and enhancement. The results show that the proposed method has high image processing ability in restoration and enhancement of fuzzy defect images. The fitting value of neural network is 0.92, which is significantly higher than that of the other two methods, indicating that the image restoration and enhancement method based on neural network has higher accuracy. Therefore, the restoration and enhancement method of fuzzy defect image based on neural network has a good restoration and enhancement effect, and can effectively meet the actual needs of people for high-quality images.</p> <p> </p>

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Zhang, Tianchi, Yong Gao, Zhiyong Wang, and Mingjun Zhang. "Underwater Image Restoration Method Based on Multi-Frame Image under Artificial Light Source." Journal of Marine Science and Engineering 11, no. 6 (June 12, 2023): 1213. http://dx.doi.org/10.3390/jmse11061213.

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This paper studies the underwater image restoration problem in autonomous operation of AUV guided by underwater visual. An improved underwater image restoration method is developed based on multi-frame neighboring images under artificial light source. At first, multi-frame neighboring images are collected during AUV approaching the targets, and a transmittance estimation method is developed based on the multi-frame images to avoid the assumption of the known normalized residual energy ratio in the traditional methods. Then, the foreground and background regions of the images are segmented by locking the small area where the background light is located. Hence, the accuracy of background light estimation is improved for the underwater mages in turbid water to improve the accuracy of image restoration. Finally, the performance of the developed underwater image restoration method is verified by the comparative results in the pool environment.

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Yang, Mengxuan, Shengnan Li, and Jinhua Zeng. "The Effects of AI-Driven Face Restoration on Forensic Face Recognition." Applied Sciences 14, no. 9 (April 29, 2024): 3783. http://dx.doi.org/10.3390/app14093783.

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In biometric recognition, face recognition is a mature and widely used technique that provides a fast, accurate, and reliable method for human identification. This paper aims to study the effects of face image restoration for forensic face recognition and then further analyzes the advantages and limitations of the four state-of-the-art face image restoration methods in the field of face recognition for forensic human image identification. In total, 100 face image materials from an open-source face image dataset are used for experiments. The Gaussian blur processing is applied to simulate the effect of blurred face images in actual cases of forensic human image identification. Four state-of-the-art AI-driven face restoration methods are used to restore the blurred face images. We use three mainstream face recognition systems to evaluate the recognition performance changes of the blurred face images and the restored face images. We find that although face image restoration can effectively remove facial noise and blurring effects, the restored images do not significantly improve the recognition performance of the face recognition systems. Face image restoration may change the original features in face images and introduce new made-up image features, thereby affecting the accuracy of face recognition. In current conditions, the improvement in face image restoration on the recognition performance of face recognition systems is limited, but it still has a positive role in the application of forensic human image identification.

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Gao, Ruoran, Huimin Lu, Adil Al-Azzawi, Yupeng Li, and Chengcheng Zhao. "DRL-FVRestore: An Adaptive Selection and Restoration Method for Finger Vein Images Based on Deep Reinforcement." Applied Sciences 13, no. 2 (January 4, 2023): 699. http://dx.doi.org/10.3390/app13020699.

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Finger vein recognition has become a research hotspot in the field of biometrics due to its advantages of non-contact acquisition, unique information, and difficulty in terms of forging or pirating. However, in the real-world application process, the extraction of image features for the biometric remains a significant challenge when the captured finger vein images suffer from blur, noise, or missing feature information. To address the above challenges, we propose a novel deep reinforcement learning-based finger vein image recovery method, DRL-FVRestore, which trained an agent that adaptively selects the appropriate restoration behavior according to the state of the finger vein image, enabling continuous restoration of the image. The behaviors of image restoration are divided into three tasks: deblurring restoration, defect restoration, and denoising and enhancement restoration. Specifically, a DeblurGAN-v2 based on the Inception-Resnet-v2 backbone is proposed to achieve deblurring restoration of finger vein images. A finger vein feature-guided restoration network is proposed to achieve defect image restoration. The DRL-FVRestore is proposed to deal with multi-image problems in complex situations. In this paper, extensive experimental results are conducted based on using four publicly accessible datasets. The experimental results show that for restoration with single image problems, the EER values of the deblurring network and damage restoration network are reduced by an average of 4.31% and 1.71%, respectively, compared to other methods. For images with multiple vision problems, the EER value of the proposed DRL-FVRestore is reduced by an average of 3.98%.

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Jiang, Xin, and Ren Jie Zhang. "Image Restoration Based on Partial Differential Equations (PDEs)." Advanced Materials Research 647 (January 2013): 912–17. http://dx.doi.org/10.4028/www.scientific.net/amr.647.912.

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Image restoration plays an important role in both the quantitative analysis and qualitative analysis of image. It directly affects the further works of analysis and processing. At present, a large number of image restoration methods are recorded in the literatures. And image restoration method based on partial differential equations(PDEs) is one of the main tools in this area. Although these methods often seem powerless for the images with complex features, image restoration method based on PDEs still has its advantages cannot be replaced. In this paper, we make a summary and appraisal on image restoration methods based on PDEs on basis of the analysis for image characteristics and predict the development trend of image restoration methods based on PDEs.

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Jia, Peng, Runyu Ning, Ruiqi Sun, Xiaoshan Yang, and Dongmei Cai. "Data-driven image restoration with option-driven learning for big and small astronomical image data sets." Monthly Notices of the Royal Astronomical Society 501, no. 1 (November 13, 2020): 291–301. http://dx.doi.org/10.1093/mnras/staa3535.

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ABSTRACT Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data-driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data-driven image restoration method based on generative adversarial networks with option-driven learning. Our method uses several high-resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.

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Wang, Kun Ling. "The Image Restoration Method Based on Patch Sparsity Propagation in Big Data Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 7 (November 20, 2018): 1072–76. http://dx.doi.org/10.20965/jaciii.2018.p1072.

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The traditional image restoration method only uses the original image data as a dictionary to make sparse representation of the pending blocks, which leads to the poor adaptation of the dictionary and the blurred image of the restoration. And only the effective information around the restored block is used for sparse coding, without considering the characteristics of image blocks, and the prior knowledge is limited. Therefore, in the big data environment, a new method of image restoration based on structural coefficient propagation is proposed. The clustering method is used to divide the image into several small area image blocks with similar structures, classify the images according to the features, and train the different feature types of the image blocks and their corresponding adaptive dictionaries. According to the characteristics of the restored image blocks, the restoration order is determined through the sparse structural propagation analysis, and the image restoration is achieved by sparse coding. The design method is programmed, and the image restoration in big data environment is realized by designing the system. Experimental results show that the proposed method can effectively restore images and has high quality and efficiency.

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Andono, Pulung Nurtantio, and Christy Atika Sari. "Remove Blur Image Using Bi-Directional Akamatsu Transform and Discrete Wavelet Transform." Scientific Journal of Informatics 9, no. 2 (November 17, 2022): 179–88. http://dx.doi.org/10.15294/sji.v9i2.34173.

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Purpose: Image is an imitation of everything that can be materialized, and digital images are taken using a machine. Although digital image capture uses machines, digital images are not free from interference. Image restoration is needed to restore the quality of the damaged image.Methods: Bi-directional Akamatsu Transform is proven to have an effective performance in reducing blur in images. Meanwhile, Discrete Wavelet Transform has been widely used in digital image processing research. We had been investigated the image restoration method by combining Bi-directional Akamatsu Transform and Discrete Wavelet Transform. Bi-directional Akamatsu Transform applied in Low-Low (LL) sub-band is the Discrete Wavelet Transform decomposition image most similar to the original image before decomposing. In this study, there are still shortcomings, including the determination of the values of N, up_enh, and down_enh, which are still manual. Manually setting the three values makes the Bi-directional Akamatsu Transform method not get the best results. With the use of machine learning methods can get better restoration results. Further testing is also needed for a more diverse and robust blur. The image data has a resolution of 256x256, 512x512, and 1024x1024. The image will be directly converted to a grey-scale image. The converted image will be given an attack model: average blur, gaussian blur, and motion blur. The image that has been attacked will apply two restoration methods: the proposed method and the Bi-direction Akatamatsu Transform. These two restoration images will then be compared using PSNR.Result: The average PSNR value from the restoration of the proposed method is 0.1446 higher than the average PSNR value from the restoration of the Bi-directional Akamatsu Transform method. When we compare it with the average PSNR value of the Akamatsu Transform restoration method, the average PSNR of the proposed method is 0.2084.Value: The combination of DWT and akamatsu transform results produce good PSNR values even though they have gone through the blurring method in image restoration.

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Lili, N. A., M. B. NorMasaina, K. Fatimah, and Y. Razali. "Image Noise Removal on Grayscale Images for Better Image Restoration." Advanced Science Letters 19, no. 8 (August 1, 2013): 2398–403. http://dx.doi.org/10.1166/asl.2013.4941.

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Tammineni, Shanmukhaprasanthi, Swaraiya Madhuri Rayavarapu, Sasibhushana Rao Gottapu, and Raj Kumar Goswami. "DIGITAL IMAGE RESTORATION USING SURF ALGORITHM." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 1 (March 31, 2024): 37–40. http://dx.doi.org/10.35784/iapgos.5373.

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In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.

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Sarojini J, Mano Andrew M, Vanisri R, and Aravindkumaran S. "UNDERWATER IMAGE RESTORATION ANDENHANCEMENT USING HYBRID ALGORITHM." international journal of engineering technology and management sciences 6, no. 6 (November 28, 2022): 250–57. http://dx.doi.org/10.46647/ijetms.2022.v06i06.040.

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Underwater image restoration and enhancement using a Hybrid algorithm to evaluate the undersea activities like underwater vehicles to carry optical imaging systems for recording. The captured images and videos frequently suffered from two displeasing problems: 1. Color distortion; 2. Poor visibility. Those factors are the most notorious threats in underwater imaging systems because the light is exponentially attenuated while penetrating through water and the strength of attenuation is color dependent. Under these inferences, an effective single underwater image restoration, and enhancement framework-based Sea-thru algorithm has been proposed for image restoration, depth estimation, and transmission compensation to enhance the image. To address the consequences of scattering and absorption, a new restoration algorithm outperformed by the state-of-the-art method both qualitatively and quantitatively. A wide variety of underwater images with various scenarios were exploited to assess the restoration performance of the proposed algorithm. The proposed underwater image restoration technique is a promising result for undersea activities that required high-quality images. Sea-thru method estimates backscatter using the dark pixels and machine learning algorithms, to create exciting opportunities for future underwater image exploration and conservation

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Miyashita, Toyokatsu, and Hiroyuki Hashiguchi. "Superresolved Image Restoration of Multifrequency Holographic Images." Japanese Journal of Applied Physics 29, S1 (January 1, 1990): 221. http://dx.doi.org/10.7567/jjaps.29s1.221.

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Simonov, Aleksey N., and Michiel C. Rombach. "Sharp-focus image restoration from defocused images." Optics Letters 34, no. 14 (July 7, 2009): 2111. http://dx.doi.org/10.1364/ol.34.002111.

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Ma, Lin, Debin Zhao, and Wen Gao. "Learning-based image restoration for compressed images." Signal Processing: Image Communication 27, no. 1 (January 2012): 54–65. http://dx.doi.org/10.1016/j.image.2011.05.004.

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Zhang, Kexin, and Hua Huo. "Image Inpainting based on Dilated Neighborhood Attention." Scientific Journal of Technology 6, no. 4 (April 22, 2024): 25–39. http://dx.doi.org/10.54691/sxsvpw35.

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In response to the phenomenon that existing image restoration algorithms have blurry edges, incoherent textures, and lack clarity and delicacy for large-area missing images, a two-stage generative adversarial image restoration algorithm based on dilated neighborhood attention is proposed. This algorithm decouples image restoration into edge structure restoration and texture structure restoration, introduces a dilated neighborhood attention mechanism, and enhances the generator's focus on important information and structures in the image by constructing a residual attention network, thereby improving the perception and utilization of texture details, resulting in more realistic image views and finer texture details. This paper introduces the Binary Cross-Entropy with Logits loss function in the discriminators of the two stages, which can help the discriminator learn how to more effectively identify real and generated images, thus improving the overall network performance. The Ranger21 optimizer is introduced to accelerate learning without affecting generalization, addressing the problem of traditional optimizers systematically staying in poor initial states. The datasets used in this paper are Paris Street View and CelebA-HQ. Comparative experiments with other image restoration methods show that both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) have improved, and the larger the mask area, the more significant the improvement. Experiments prove that the images restored by the proposed algorithm have more reasonable structures and richer details, and the image restoration effect is superior.

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Senkamalavalli, Dr R. "Restoration of Obscured Images." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 1106–12. http://dx.doi.org/10.22214/ijraset.2023.56682.

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Abstract: For the aim of exploring the deep undersea world map, a picture of high quality without interfering objects is preferred. However, within the water, the image quality tends to be hampered by light scattering, water density, and light attenuation. effects. Besides, the dynamic interference may affect the important underwater map. during this paper, we proposed a multi-step and all-around underwater image processing system, especially for the underwater images taken in succession to enhance the image quality, remove the dynamic interference, and reconstruct the image. The first step involves utilizing the dark channel approach together with the improved gray world algorithm for brightness adaptation and color correction. Initially, it identifies and removes a dynamic interference regarding image enhancement. Secondly, we applied an upgraded total variation model to patch the blank at the value of resolution. Finally, the super-resolution of the small print is realized by applying an improved BP network. After simulation and experiments, our system proved to realize ideal results of image enhancement and reconstruction.

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Liu, Zihan. "Literature Review on Image Restoration." Journal of Physics: Conference Series 2386, no. 1 (December 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2386/1/012041.

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Abstract Image restoration is an essential part in the field of computer vision, which aims at predicting and filling the pixels of the missing images to achieve satisfactory visual effects, it has extensive application value in the fields of film and television special effects production, image editing, digital cultural heritage protection and virtual reality. With the introduction and application of the concept of deep learning in recent years, it has been widely studied in the academic and industrial fields, the performance of image restoration has been significantly improved, so that this long-standing research topic has once again aroused widespread concern and heated discussion on the social level. In order to enable more researchers to explore the theory of image restoration and its development, this paper reviews the related technologies in this field: firstly, the traditional image restoration methods are described, secondly, the background of deep learning is introduced, then the image restoration methods based on deep learning are described, subsequently, the several deep-learning based methods are compared and analyzed, finally, the future research direction and emphasis of image restoration are analyzed and prospected.

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Bai, Yang, Zheng Tan, Qunbo Lv, and Min Huang. "A Deconvolutional Deblurring Algorithm Based on Short- and Long-Exposure Images." Sensors 22, no. 5 (February 25, 2022): 1846. http://dx.doi.org/10.3390/s22051846.

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An iterative image restoration algorithm, directed at the image deblurring problem and based on the concept of long- and short-exposure deblurring, was proposed under the image deconvolution framework by investigating the imaging principle and existing algorithms, thus realizing the restoration of degraded images. The effective priori side information provided by the short-exposure image was utilized to improve the accuracy of kernel estimation, and then increased the effect of image restoration. For the kernel estimation, a priori filtering non-dimensional Gaussianity measure (BID-PFNGM) regularization term was raised, and the fidelity term was corrected using short-exposure image information, thus improving the kernel estimation accuracy. For the image restoration, a P norm-constrained relative gradient regularization term constraint model was put forward, and the restoration result realizing both image edge preservation and texture restoration effects was acquired through the further processing of the model results. The experimental results prove that, in comparison with other algorithms, the proposed algorithm has a better restoration effect.

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Sawant, Sakshi. "Underwater Image Restoration System." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 175–77. http://dx.doi.org/10.22214/ijraset.2023.57286.

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Abstract: Underwater imaging poses significant challenges due to light absorption, scattering, and color distortion. This paper introduces an innovative underwater image restoration system designed to enhance the visibility and analytical capabilities of underwater imaging. The proposed system employs advanced image processing techniques to address the inherent issues associated with underwater photography. The methodology involves the development of a model that accounts for the optical properties of water, including attenuation and scattering. By leveraging this model, the system corrects color distortions and enhances contrast, leading to improved clarity in underwater images. Additionally, a novel algorithm is employed to reduce the impact of particulate matter, such as suspended sediments, contributing to a clearer representation of the underwater scene. The system incorporates machine learning approaches for adaptive filtering, allowing it to dynamically adjust parameters based on environmental conditions. This adaptability enables the restoration system to perform effectively across a range of underwater scenarios, from clear to turbid waters.

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H. Mohammed, Omar, and Basil Sh. Mahmood. "Advance in Image and Audio Restoration and their Assessments: A Review." International Journal of Computer Science & Engineering Survey 12, no. 2 (April 30, 2021): 1–16. http://dx.doi.org/10.5121/ijcses.2021.12201.

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Image restoration is the process of restoring the original image from a degraded one. Images can be affected by various types of noise, such as Gaussian noise, impulse noise, and affected by blurring, which is happened during image recordings like motion blur, Out-of-Focus Blur, and others. Image restoration techniques are used to reverse the effect of noise and blurring. Restoration of distorted images can be done using some information about noise and the blurring nature or without any knowledge about the image degradation process. Researchers have proposed many algorithms in this regard; in this paper, different noise and degradation models and restoration methods will be discussed and review some researches in this field.

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Li, Chen. "A Partial Differential Equation-Based Image Restoration Method in Environmental Art Design." Advances in Mathematical Physics 2021 (October 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/4040497.

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With the rapid development of networks and the emergence of various devices, images have become the main form of information transmission in real life. Image restoration, as an important branch of image processing, can be applied to real-life situations such as pixel loss in image transmission or network prone to packet loss. However, existing image restoration algorithms have disadvantages such as fuzzy restoration effect and slow speed; to solve such problems, this paper adopts a dual discriminator model based on generative adversarial networks, which effectively improves the restoration accuracy by adding local discriminators to track the information of local missing regions of images. However, the model is not optimistic in generating reasonable semantic information, and for this reason, a partial differential equation-based image restoration model is proposed. A classifier and a feature extraction network are added to the dual discriminator model to provide category, style, and content loss constraints to the generative network, respectively. To address the training instability problem of discriminator design, spectral normalization is introduced to the discriminator design. Extensive experiments are conducted on a data dataset of partial differential equations, and the results show that the partial differential equation-based image restoration model provides significant improvements in image restoration over previous methods and that image restoration techniques are exceptionally important in the application of environmental art design.

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Stern, Adrian, and Norman S. Kopeika. "General restoration filter for vibrated-image restoration." Applied Optics 37, no. 32 (November 10, 1998): 7596. http://dx.doi.org/10.1364/ao.37.007596.

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Patil, Basavaraj, Ananya C M, Anusha Vinod Nayak, Ashrita S. Naik, and Harshitha B R. "Restoring Deteriorated Images using Deep Learning Techniques: Region Filling, Median Filter." International Journal of Engineering Research in Computer Science and Engineering 9, no. 7 (July 21, 2022): 33–39. http://dx.doi.org/10.36647/ijercse/09.07.art009.

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Image restoration is a technique for recovering images from corrupted images that have blur and noise, lowering the image's quality. Motion blur, low resolution, moisture in the atmosphere, and other factors can all contribute to image noise. For noise removal, there are a variety of restoration techniques and a spatial domain filter. To eliminate blur and scratches in deteriorated photographs, an image restoration method has been developed. Deep learning has gained popularity as a method for image restoration during the last few years. Denoising and other image restoration operations are necessary steps in many image processing applications. Image fusion using the stacked median operator, low resolution detail improvement using guided super sampling, and repeated visual consistency assessment and refining are the three processes in the restoration process. Two VAEs (Variational Autoencoders) are trained in this model to translate old and clean pictures into two latent spaces, respectively. This is due to the fact that they are all using supervised learning, which is a difficulty created by the domain gap between the original image and the ones synthesized for training. The suggested project offers a cost-effective solution that can deal with noise, picture rotations, and occlusions.

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Zhang, Tianchi, Qian Li, Yusong Li, and Xing Liu. "Underwater Optical Image Restoration Method for Natural/Artificial Light." Journal of Marine Science and Engineering 11, no. 3 (February 22, 2023): 470. http://dx.doi.org/10.3390/jmse11030470.

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This paper investigates the underwater optical image restoration method under the background of underwater target detection based on optical vision in AUVs. The light source used for AUV detection is different when the AUV operates in different depths. The natural light source is used in shallow water and the artificial light source is used in deep water. This paper investigates underwater optical image restoration in these two light conditions. Aiming at the problem of image blurring in underwater optical images, the traditional underwater image restoration method based on scattering model can obtain satisfactory image restoration performance in natural light conditions. However, it cannot obtain the same image restoration result in artificial light conditions. To solve this problem, this paper presents an improved underwater optical image restoration method based on the scattering model. The scattering model and power spectrum are used to solve the initial parameters of the filter, and the parameters are optimized based on an evaluation index. The index of image definition is introduced to evaluate the restoration performance and to achieve the satisfactory image restoration result in both natural light and artificial light conditions. The effectiveness of the presented method is verified by experiments.

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Sethi, Rajni, and Sreedevi Indu. "Fusion of Underwater Image Enhancement and Restoration." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 03 (July 5, 2019): 2054007. http://dx.doi.org/10.1142/s0218001420540075.

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Optical properties of water distort the quality of underwater images. Underwater images are characterized by poor contrast, color cast, noise and haze. These images need to be pre-processed so as to get some information. In this paper, a novel technique named Fusion of Underwater Image Enhancement and Restoration (FUIER) has been proposed which enhances as well as restores underwater images with a target to act on all major issues in underwater images, i.e. color cast removal, contrast enhancement and dehazing. It generates two versions of the single input image and these two versions are fused using Laplacian pyramid-based fusion to get the enhanced image. The proposed method works efficiently for all types of underwater images captured in different conditions (turbidity, depth, salinity, etc.). Results obtained using the proposed method are better than those for state-of-the-art methods.

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Sun, Guoxin, Xiong Yan, Huizhe Wang, Fei Li, Rui Yang, Jing Xu, Xin Liu, Xiaomao Li, and Xiao Zou. "Color restoration based on digital pathology image." PLOS ONE 18, no. 6 (June 28, 2023): e0287704. http://dx.doi.org/10.1371/journal.pone.0287704.

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Objective Protective color restoration of faded digital pathology images based on color transfer algorithm. Methods Twenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The color transfer algorithm was applied to restore the color of the faded images; Adobe Lightroom Classic software presented the histogram of the image color distribution; UNet++ cell recognition segmentation model was used to identify the color restored images; Natural Image Quality Evaluator (NIQE), Information Entropy (Entropy), and Average Gradient (AG) were applied to evaluate the quality of the restored images. Results The restored image color met the diagnostic needs of pathologists. Compared with the faded images, the NIQE value decreased (P<0.05), Entropy value increased (P<0.01), and AG value increased (P<0.01). The cell recognition rate of the restored image was significantly improved. Conclusion The color transfer algorithm can effectively repair faded pathology images, restore the color contrast between nucleus and cytoplasm, improve the image quality, meet the diagnostic needs and improve the cell recognition rate of the deep learning model.

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Fu, Zhenqi, Huangxing Lin, Yan Yang, Shu Chai, Liyan Sun, Yue Huang, and Xinghao Ding. "Unsupervised Underwater Image Restoration: From a Homology Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 643–51. http://dx.doi.org/10.1609/aaai.v36i1.19944.

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Underwater images suffer from degradation due to light scattering and absorption. It remains challenging to restore such degraded images using deep neural networks since real-world paired data is scarcely available while synthetic paired data cannot approximate real-world data perfectly. In this paper, we propose an UnSupervised Underwater Image Restoration method (USUIR) by leveraging the homology property between a raw underwater image and a re-degraded image. Specifically, USUIR first estimates three latent components of the raw underwater image, i.e., the global background light, the transmission map, and the scene radiance (the clean image). Then, a re-degraded image is generated by randomly mixing up the estimated scene radiance and the raw underwater image. We demonstrate that imposing a homology constraint between the raw underwater image and the re-degraded image is equivalent to minimizing the restoration error and hence can be used for the unsupervised restoration. Extensive experiments show that USUIR achieves promising performance in both inference time and restoration quality.

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Lv, Hui Xian, Zhi Gang Zhao, and Yan Feng Xu. "A Novel Image Contrast Restoration Algorithm for Fog." Advanced Materials Research 709 (June 2013): 534–37. http://dx.doi.org/10.4028/www.scientific.net/amr.709.534.

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Images captured in fog suffer from low contrast, restoration of fog- degraded images are needed. In this paper, a novel algorithm of image restoration based on wavelet semi-soft threshold is presented. The results show detail restoration and de-noising are improved effectively comparing with Histogram equalization and homomorphic filtering method. It can be concluded that the new algorithm enhanced the contrast of fog-degraded image well.

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Chyan, Phie, and Tri Saptadi. "Image Restoration Using Deep Learning Based Image Completion." Jurnal Sisfokom (Sistem Informasi dan Komputer) 12, no. 3 (November 3, 2023): 335–40. http://dx.doi.org/10.32736/sisfokom.v12i3.1699.

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Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the latest relying on artificial intelligence algorithms. This study aims to develop and implement an image completion model based on deep learning with the transfer learning method from the completion.net architecture. Using the Facesrub training dataset consisting of a collection of unique facial photos allows the model to understand facial attributes better. Compared to conventional image completion based on image patches, the method developed in this study can perform image filling in image gaps with more realistic results. Based on visual tests conducted on respondents, the results obtained enable respondents to understand all the information represented by the restored image, similar to the original image.

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Typke, D., R. Hegerl, and J. Kleinz. "Image restoration for biological specimens using external TEM control and electronic image recording." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (August 1992): 1000–1001. http://dx.doi.org/10.1017/s0424820100129632.

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Images of non-crystalline biological specimens embedded in vitreous ice or carbohydrates have to be recorded at rather high defocus to obtain sufficient contrast. Therefore resolution is normally rather limited. Though it is well-known that image restoration from focus series can provide more complete information, this or related techniques have only rarely been used. However, the recent facilities of external TEM control and electronic image recording suggest the restoration technique should be revived and adapted to the needs of biological structure investigation. As autofocus methods have been shown to work rather accurately and focus steps can be calibrated in advance, it can be expected that the restoration can, in principle, be carried out on line and quasi blind. An additional advantage of image restoration from focus series is that it can be used, particularly in case of rather thick ice layers, to separate the phase part of the image function from most of the background due to multiple scattering by combining under- and overfocus images.

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Journal articles on the topic 'Image restoration'

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