Relevant bibliographies by topics / HAZY IMAGE

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'HAZY IMAGE'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "HAZY IMAGE"

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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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Dissertations / Theses on the topic "HAZY IMAGE"

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Zhao, Nilu. "Haze measurements through image analysis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/92216.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 28).
In the recent years, Singapore has been affected by haze caused by slash-and-bum fires in Indonesia. Currently, haze concentration is measured by filtering air samples at various stations in Singapore. In this thesis, optical approaches to haze measurements are explored. Images of haze were taken in fifteen minute intervals in June, 2013. These images were analyzed to obtain image contrast, and power spectral density functions. The power spectral density functions were characterized by maximum power, full width at half maximum, second and third moments, and exponential fit. Out of these methods, contrast and exponential fit results showed trend to the Pollutant Standards Index (PSI) values provided by the National Environmental Agency (NEA). Further studies on mapping contrast to PSI values are recommended.
by Nilu Zhao.
S.B.
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Basinger, John A. "Grain Boundary Character Distribution in the HAZ of Friction Stir-Processed Al 7075 T7." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1046.pdf.

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Arigela, Sai Babu. "A Self Tunable Transformation Function for Enhancement of Images Captured in Complex Lighting and Hazy Weather Conditions." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449185835.

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Pettersson, Niklas. "GPU-Accelerated Real-Time Surveillance De-Weathering." Thesis, Linköpings universitet, Datorseende, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97401.

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A fully automatic de-weathering system to increase the visibility/stability in surveillance applications during bad weather has been developed. Rain, snow and haze during daylight are handled in real-time performance with acceleration from CUDA implemented algorithms. Video from fixed cameras is processed on a PC with no need of special hardware except an NVidia GPU. The system does not use any background model and does not require any precalibration. Increase in contrast is obtained in all haze/rain/snow-cases while the system lags the maximum of one frame during rain or snow removal. De-hazing can be obtained for any distance to simplify tracking or other operating algorithms on a surveillance system.
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Francis, John W. "Pixel-by pixel reduction of atmospheric haze effects in multispectral digital imagery of water /." Online version of thesis, 1989. http://hdl.handle.net/1850/11359.

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Abbott, Joshua E. "Interactive Depth-Aware Effects for Stereo Image Editing." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3712.

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This thesis introduces methods for adding user-guided depth-aware effects to images captured with a consumer-grade stereo camera with minimal user interaction. In particular, we present methods for highlighted depth-of-field, haze, depth-of-field, and image relighting. Unlike many prior methods for adding such effects, we do not assume prior scene models or require extensive user guidance to create such models, nor do we assume multiple input images. We also do not require specialized camera rigs or other equipment such as light-field camera arrays, active lighting, etc. Instead, we use only an easily portable and affordable consumer-grade stereo camera. The depth is calculated from a stereo image pair using an extended version of PatchMatch Stereo designed to compute not only image disparities but also normals for visible surfaces. We also introduce a pipeline for rendering multiple effects in the order they would occur physically. Each can be added, removed, or adjusted in the pipeline without having to reapply subsequent effects. Individually or in combination, these effects can be used to enhance the sense of depth or structure in images and provide increased artistic control. Our interface also allows editing the stereo pair together in a fashion that preserves stereo consistency, or the effects can be applied to a single image only, thus leveraging the advantages of stereo acquisition even to produce a single photograph.
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Toro, León Paulina Fernanda. "Preferencias por imagen sialográfica adquirida con radiografía panorámica digital y con tomografía computarizada de haz cónico." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/117298.

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Trabajo de Investigación Requisito para optar al Título de Cirujano Dentista
Autor no autoriza el acceso a texto completo de su tesis en el Portal de Tesis Electrónicas
ntroducción: La sialografía mediante Tomografía Computarizada de Haz Cónico (TCHC) ha presentado un interés creciente de la comunidad internacional de especialistas radiólogos en los últimos cinco años. En este contexto se pretendió determinar la preferencia de un grupo de especialistas en Radiología Oral y Maxilofacial entre la imagen sialográfica obtenida mediante Radiografía Panorámica Digital (RPD) y aquella obtenida mediante TCHC. Material y Método: Se realizó un estudio descriptivo de corte transversal. La muestra se compuso de diez especialistas en Radiología Oral y Maxilofacial, quienes definieron su preferencia, mediante una encuesta, por la imagen sialográfica adquirida mediante RPD o mediante TCHC en cuanto a calidad de imagen, identificación de estructuras anatómicas, y reconocimiento de patología glandular. Resultados: Las observaciones mostraron que la sialografía mediante RPD fue la opción preferida respecto a nitidez de imagen, mientras que la sialografía con TCHC fue preferida para evaluar el lóbulo profundo de la glándula parótida. Ambos exámenes fueron igualmente preferidos para visualizar el conducto excretor parotídeo y los conductillos de segundo orden, y no existió marcada preferencia entre uno u otro examen para el reconocimiento de patología glandular. Conclusión: Ambos exámenes presentan ventajas particulares a la hora de evaluar patología glandular mediante sialografía. Se sugiere investigar, en futuros estudios, las razones detrás de estas preferencias, que podrían darnos pistas de la potencialidad de uso de la Sialografía combinada con TCHC en nuestro país.
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Clark, Tad Dee. "An Analysis of Microstructure and Corrosion Resistance in Underwater Friction Stir Welded 304L Stainless Steel." Diss., BYU ScholarsArchive, 2005. http://contentdm.lib.byu.edu/ETD/image/etd872.pdf.

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Zepeda, Barrios Alejandro. "Evaluación de la Evolución Temporal en Tumores Pulmonares Tratados con Radioterapia Estereotáctica Corporal a partir de Rasgos Extraídos de las Imágenes de Tomografía Computarizada con Haz Cónico." Tesis de maestría, Universidad Autónoma del Estado de México, 2021. http://hdl.handle.net/20.500.11799/111960.

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Tesis de maestría
En los tratamientos de radioterapia estereotáctica corporal (SBRT) es cada vez más común el uso de sistemas de tomografía computarizada por haz cónico (CBCT) montados en los aceleradores lineales, para la adquisición de imágenes tomográficas que son utilizadas para la verificación y corrección -si es el caso- del posicionamiento del paciente durante el tratamiento. En radioterapia es de importancia mayúscula asegurar que la posición del paciente sea la deseada y en SBRT esto adquiere aún mayor importancia ya que la dosis absorbida utilizada en estos procedimientos es mayor que en los casos de radioterapia de fraccionamientos convencionales (típicamente entre 10 Gy y 20 Gy por sesión de tratamiento, mientras que en otros tratamientos puede estar entre 2 Gy y 3 Gy). En el curso de SBRT, se obtiene un conjunto de imágenes de CBCT por cada sesión de tratamiento, que se compara con la tomografía de planeación para verificar que la colocación del paciente sea la indicada, regularmente las series de CBCT ya no son utilizadas para otro fin. Sin embargo, hay estudios que han demostrado que estas imágenes pueden ser utilizadas para obtener información cuantitativa de los efectos del tratamiento durante su administración, particularmente cuando se otorga en lesiones pulmonares usando SBRT. En este trabajo se buscó utilizar las imágenes de pacientes, obtenidas mediante CBCT, durante el curso de un tratamiento de SBRT de pulmón (específicamente al inicio, en una etapa intermedia y al final), para obtener rasgos cuantitativos que puedan brindar información acerca del tejido tumoral, ya sea debido a cambios en su morfología, en su intensidad de pixeles o en su textura, asociados a los efectos del tratamiento, destacando que esta evaluación se llevó a cabo solamente durante el tratamiento.. Los rasgos cuantitativos utilizados en el estudio se seleccionaron basándose en su coeficiente de variación, que nos proporciona información de su confiabilidad. A partir de las imágenes de CBCT, una vez seleccionados los rasgos, se estudió su evolución temporal del tejido tumoral a lo largo del tratamiento.
Ninguno
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Berny, Myriam. "High-temperature tests for ceramic matrix composites : from full-field regularised measurements to thermomechanical parameter identification." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST028.

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Cette thèse a pour objectif de développer une méthode de mesure de champs par corrélation d’images numériques (CIN) à haute température couplée à des mesures thermiques sur une éprouvette technologique en CMC sollicitée dans des conditions thermiques représentatives d’un environnement moteur et de mettre en place une méthodologie d’identification des propriétés thermiques et thermomécaniques du matériau, en quantifiant à chaque étape de la chaîne les incertitudes associées aux quantités d’intérêt et en les réduisant. Il a pour cela été nécessaire de traiter les défis inhérents à la CIN à chaud, que ce soit au niveau de l’acquisition des images (saturation, perte du contraste) ou de la mesure (artefacts dus à l’effet mirage, aussi appelé "brume de chaleur").Ces travaux ont ainsi donné lieu au développement d’un protocole d’étalonnage d’un banc multi-instrumenté par l’utilisation soit d’une mire in-situ, soit par auto-étalonnage en utilisant l’éprouvette elle-même et son environnement. Les mesures de déplacements 3D surfaciques (approches de stéréocorrélation globales) et les mesures thermiques ont permis de mettre en évidence ce phénomène de brume de chaleur. Des stratégies de régularisation spatiotemporelles des déplacements mesurés ont été proposées et ont permis d’obtenir des résultats satisfaisants (réduction significative des incertitudes de mesure). De même, des approches par réduction de modèles (POD) ont permis de traiter les données thermiques et de quantifier les incertitudes associées aux phénomènes convectifs. Enfin, un algorithme de recalage de modèle éléments finis pondéré sur les données de températures et de déplacements a été implémenté en vue d’identifier un ensemble de propriétés thermiques et thermomécaniques, en tenant compte de la sensibilité de chaque paramètre par rapport aux incertitudes de mesures
The aim of this thesis is firstly to develop procedures of full-field measurements with Digital Image Correlation (DIC), coupled to thermal measurements, suitable for high-temperature experiments on CMC specimens under thermal conditions representative of an engine environment. Secondly, a methodology is proposed for identifying the thermal and thermomechanical properties of the material, quantifying at each stage of the chain the uncertainties associated with the quantities of interest and strategies to reduce them. It was necessary to deal with the challenges due to high temperatures, especially for DIC, either in terms of acquisition (saturation, loss of contrast) or measurement (artefacts due to the mirage effect, also called "heat haze effect").This work has led to the development of a calibration protocol for a multi-instrumented bench using either an in-situ calibration target or by self-calibration using the specimen itself and its environment. 3D surface displacement measurements (with global stereocorrelation approaches) and thermal measurements have made it possible to highlight the heat haze effect phenomenon. Spatiotemporal regularisation strategies of the measured displacements were proposed and allowed satisfactory results to be obtained (significant reduction of measurement uncertainties). Similarly, model reduction approaches (POD) have been used to process thermal data and quantify the uncertainties associated with convective phenomena. Finally, a weighted Finite-Element Model Updating (FEMU) algorithm on both temperature and displacement data was implemented in order to identify a set of thermal and thermomechanical properties, taking into account the sensitivity of each parameter with regard to measurement uncertainties
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Books on the topic "HAZY IMAGE"

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Sin tetas no hay paraíso. Madrid: Editorial el Tercer Nombre, 2006.

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Sin tetas no hay paraíso. Bogotá, Colombia: Quintero, 2005.

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Moreno, Gustavo Bolívar. Sin tetas no hay paraíso. 3rd ed. Bogotá, Colombia: Quintero Editores, 2005.

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Ubaldo de Casanova y Todolí. Hay algo que no funciona: La imagen de un mundo ajeno a la realidad. Salamanca: Amarú Ediciones, 2011.

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Esponda, Juan González. "Ya no hay tributo, ni rey": De profetas y mesías en la insurrección de 1712 en la provincia de Chiapa. San Cristóbal de Las Casas, Chiapas: Secretaría de Pueblos y Culturas Indígenas, 2013.

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An analysis of Neptune's stratospheric haze using high-phase-angle voyager images. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Lincoln's Boys: John Hay, John Nicolay, and the War for Lincoln's Image. Penguin Publishing Group, 2014.

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Lincoln's boys: John Hay, John Nicolay, and the war for Lincoln's image. 2014.

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Dave, Strain, ed. Black Hills hay camp: Images and perspectives of early Rapid City. Rapid City, S.D: Dakota West Books, 1989.

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Black Hills hay camp: Images and perspectives of early Rapid City. Fenske Print, 1989.

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Book chapters on the topic "HAZY IMAGE"

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El Khoury, Jessica, Jean-Baptiste Thomas, and Alamin Mansouri. "A Spectral Hazy Image Database." In Lecture Notes in Computer Science, 44–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51935-3_5.

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Zhao, Lingyun, Miles Hansard, and Andrea Cavallaro. "Pop-up Modelling of Hazy Scenes." In Image Analysis and Processing — ICIAP 2015, 306–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_28.

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Koranga, Pushpa, Sumitra Singar, and Sandeep Gupta. "Single Image Dehazing Techniques for Different Types of Hazy Images." In Applied Computational Technologies, 383–94. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2719-5_36.

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Kumar, Balla Pavan, Arvind Kumar, and Rajoo Pandey. "Fast Adaptive Image Dehazing and Details Enhancement of Hazy Images." In Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences, 215–23. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8742-7_18.

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Agrawal, Subhash Chand, and Anand Singh Jalal. "Visibility Improvement of Hazy Image Using Fusion of Multiple Exposure Images." In Smart Innovations in Communication and Computational Sciences, 321–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5345-5_29.

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Sharma, Divya, Shilpa Sharma, and Vaibhav Bhatnagar. "Foggy–Hazy License Plate Image Data Collection and Feature Extraction." In Proceedings of International Conference on Data Analytics and Insights, ICDAI 2023, 233–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3878-0_20.

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Som, S., P. K. Gayen, S. Bakshi, and S. Mondal. "Vehicle License Plate Image Preprocessing Strategy Under Fog/Hazy Weather Conditions." In Studies in Autonomic, Data-driven and Industrial Computing, 277–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7305-4_27.

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Agrawal, S. C. "Improving visibility of hazy images using image enhancement-based approaches through the fusion of multiple exposure images." In Smart Computing, 204–13. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003167488-26.

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Zhang, Zhengxi, Liang Zhao, Yunan Liu, Shanshan Zhang, and Jian Yang. "Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes." In Computer Vision – ACCV 2020, 119–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69538-5_8.

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Singh, Satbir, Asifa Mehraj Baba, Md Imtiyaz Anwar, Ayaz Hussain Moon, and Arun Khosla. "Visibility Improvement in Hazy Conditions via a Deep Learning Based Image Fusion Approach." In Communications in Computer and Information Science, 410–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81462-5_37.

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Conference papers on the topic "HAZY IMAGE"

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Cheng, De, Yan Li, Dingwen Zhang, Nannan Wang, Xinbo Gao, and Jiande Sun. "Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/119.

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Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe image dehazing. To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios. Specifically, the dehazing network is optimized under the consistency-regularized framework with the proposed Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the negative information to facilitate the traditional positive-orient dehazing objective function, by squeezing the dehazed image to its clean target from different directions. Meanwhile, the consistency regularization keeps consistent outputs given multi-level hazy images, thus improving the model robustness. Extensive experimental results on two synthetic and three real-world datasets demonstrate that our method significantly surpasses the state-of-the-art approaches.
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Liang, Yudong, Bin Wang, Wangmeng Zuo, Jiaying Liu, and Wenqi Ren. "Self-supervised Learning and Adaptation for Single Image Dehazing." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/159.

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Existing deep image dehazing methods usually depend on supervised learning with a large number of hazy-clean image pairs which are expensive or difficult to collect. Moreover, dehazing performance of the learned model may deteriorate significantly when the training hazy-clean image pairs are insufficient and are different from real hazy images in applications. In this paper, we show that exploiting large scale training set and adapting to real hazy images are two critical issues in learning effective deep dehazing models. Under the depth guidance estimated by a well-trained depth estimation network, we leverage the conventional atmospheric scattering model to generate massive hazy-clean image pairs for the self-supervised pre-training of dehazing network. Furthermore, self-supervised adaptation is presented to adapt pre-trained network to real hazy images. Learning without forgetting strategy is also deployed in self-supervised adaptation by combining self-supervision and model adaptation via contrastive learning. Experiments show that our proposed method performs favorably against the state-of-the-art methods, and is quite efficient, i.e., handling a 4K image in 23 ms. The codes are available at https://github.com/DongLiangSXU/SLAdehazing.
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Ancuti, Codruta O., Cosmin Ancuti, and Radu Timofte. "NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00230.

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Gibson, Kristofor B., and Truong Q. Nguyen. "Hazy image modeling using color ellipsoids." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6115830.

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Huang, Shan, Hao Chang, Wei Wu, and Zhu Li. "DPGIR: SIFT Recovery from a Hazy Image." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859859.

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Gui, Jie, Xiaofeng Cong, Yuan Cao, Wenqi Ren, Jun Zhang, Jing Zhang, and Dacheng Tao. "A Comprehensive Survey on Image Dehazing Based on Deep Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/604.

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The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed dehazing methods. Firstly, we conclude the commonly used datasets, loss functions and evaluation metrics. Secondly, we group the existing researches of ID into two major categories: supervised ID and unsupervised ID. The core ideas of various influential dehazing models are introduced. Finally, the open issues for future research on ID are pointed out.
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Li, Binghan, Yindong Hua, and Mi Lu. "Object Detection in Hazy Environment Enhanced by Preprocessing Image Dataset with Synthetic Haze." In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020. http://dx.doi.org/10.1109/csci51800.2020.00298.

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Li, Dajian, Wei Jia, Wei Sun, Penghui Li, Chunyu Zhao, and Xumeng Chen. "Image Enhancement Focusing on Hazy and Non-uniform Illumination Images." In 2015 International Conference on Electronic Science and Automation Control. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/esac-15.2015.50.

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Yuan, Zhiyu, Yuhang Li, and Jianfei Yang. "Improving Hazy Image Recognition by Unsupervised Domain Adaptation." In 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2022. http://dx.doi.org/10.1109/icarcv57592.2022.10004270.

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Chen, Kai, Juping Liu, Chuheng Chen, Zhe Wang, and Mingye Ju. "Contrast Restoration of Hazy Image in HSV Space." In 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2021. http://dx.doi.org/10.1109/wcsp52459.2021.9613421.

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Reports on the topic "HAZY IMAGE"

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Du, Y., B. Guindon, and J. Cihlar. Haze detection and removal in high resolution satellite image with wavelet analysis. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219726.

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