Relevant bibliographies by topics / Dark channel prior

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  2. Dissertations / Theses
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Academic literature on the topic 'Dark channel prior'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Dark channel prior"

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Oh, Hyeun-Soo, Young-Soo Han, and Kyung-Ho Lee. "White Channel Prior: A Study of Single Image Dehaze by Using Improved Dark Channel Prior." Korean Journal of Computational Design and Engineering 26, no. 4 (2021): 345–54. http://dx.doi.org/10.7315/cde.2021.345.

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Roh, Chang Su, Yeon Gyo Kim, and Ui Pil Chong. "A LabVIEW-based Video Dehazing using Dark Channel Prior." Journal of Korea Multimedia Society 20, no. 2 (2017): 101–7. http://dx.doi.org/10.9717/kmms.2017.20.2.101.

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Gao, Renjie, Yi Wang, Min Liu, and Xin Fan. "Fast algorithm for dark channel prior." Electronics Letters 50, no. 24 (2014): 1826–28. http://dx.doi.org/10.1049/el.2014.2884.

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Pan, Jinshan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang. "Deblurring Images via Dark Channel Prior." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 10 (2018): 2315–28. http://dx.doi.org/10.1109/tpami.2017.2753804.

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Guan, Jinge, Miao Ma, and Yongsheng Huo. "Underwater polarimetric dark channel prior descattering." Optics & Laser Technology 175 (August 2024): 110864. http://dx.doi.org/10.1016/j.optlastec.2024.110864.

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Li, Chuan, Changjiu Yuan, Hongbo Pan, et al. "Single-Image Dehazing Based on Improved Bright Channel Prior and Dark Channel Prior." Electronics 12, no. 2 (2023): 299. http://dx.doi.org/10.3390/electronics12020299.

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Single-image dehazing plays a significant preprocessing role in machine vision tasks. As the dark-channel-prior method will fail in the sky region of the image, resulting in inaccurately estimated parameters, and given the failure of many methods to address a large band of haze, we propose a simple yet effective method for single-image dehazing based on an improved bright prior and dark channel prior. First, we use the Otsu method by particle swarm optimization to divide the hazy image into sky regions and non-sky regions. Then, we use the improved bright channel prior and dark channel prior t
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Lin, Bai Lin. "Image Haze Removal Based Dark Channel Prior." Applied Mechanics and Materials 536-537 (April 2014): 186–91. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.186.

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This paper describes the image based on dark channel prior to fog. Dark colors from the statistical laws of outdoor priori no fog image database, it was such a key observation was based on the fact that - the vast majority of each local area outdoor image without fog are present strength of certain of at least one color channel low value pixels. Using this model building, directly estimate the concentration of fog and mist removal recover high quality interference image. Experiments show that the dark channel prior to remove the image haze becomes simple and effective.
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Jaiswal, Disha M. "Haze Removal System using Dark Channel Prior." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 301–9. http://dx.doi.org/10.22214/ijraset.2021.34984.

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Mostly in winter season, the Northern area of India is mostly affected due to heavy haze. The road traffic and air traffic is affected due to poor visibility. According to the survey of Ministry of Road Transport and Highways of India, the number of accident due to poor visibility increasing every year. Hence there is need of robust algorithm to enhance the visibility of the camera feed. In the proposed approach, image dehazing algorithm has been present using dark channel prior. The proposed algorithm is developed for outdoor images. The proposed system processed the image through dark channe
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Suo, Haotong, Jinge Guan, Miao Ma, et al. "Dynamic Dark Channel Prior Dehazing with Polarization." Applied Sciences 13, no. 18 (2023): 10475. http://dx.doi.org/10.3390/app131810475.

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For traditional dark channel prior (DCP) imaging through haze environments, intensity information acts as the carrier to acquire the reflective character of the dehazed target image. We introduce polarization as auxiliary information into the traditional DCP dehazed process for achieving better imaging performance that can improve target detection or target tracking. In this paper, a polarization imaging system with a split-amplitude structure is designed to enable real-time polarization acquisition of transient scenes. The experimental results show that besides descattering, the proposed meth
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Subramanyam, Sana. "Enhanced Image Dehazing Using Bright and Dark Channel Prior." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49041.

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ABSTRACT - Visibility degradation caused by haze presents a major challenge for computer vision systems in outdoor environments. This project introduces an enhanced image dehazing method based on Bright and Dark Channel Prior (BDCP), an improvement over traditional dark channel technique. By incorporating both bright and dark channel information, the method more effectively estimates the transmission map and atmospheric light, resulting in clearer and more natural dehazed images. The proposed algorithm refines visual quality through a fusion-based enhancement and edge-preserving filtering. Ext
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Dissertations / Theses on the topic "Dark channel prior"

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Guo, Jing-Ming, Jin-Yu Syue, Vincent Radzicki, and Hua Lee. "FUSION-BASED AND FLICKER-FREE DEFOGGING." International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624187.

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Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improves the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The system
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Ahmadvand, Samaneh. "Efficient Visibility Restoration Method Using a Single Foggy Image in Vehicular Applications." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38486.

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Foggy and hazy weather conditions considerably effect visibility distance which impacts speed, flow of traffic, travel time delay and increases the risk accidents. Bad weather condition is considered a cause of road accidents, since it the poor conditions can effect drivers field of vision. In addition, fog, haze and mist can have negative influences on visual applications in the open air since they decrease visibility by lowering the contrast and whitening the visible color palette. The poor visibility in these images leads to some failures in recognition and detection of the outdoor object s
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Ould, Amer Khadidja. "Prétraitements des images sous-marines basés sur la polarisation et le filtrage fréquentiel : application offshore Enhancing underwater optical imaging by using a low-pass polarization filter, in Optics Express 27(2), 2019." Thesis, Brest, 2019. http://www.theses.fr/2019BRES0049.

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L'étude du milieu sous-marin nécessite des avancées technologiques importantes notamment en ce qui concerne le développement des véhicules sous-marins autonomes et en particulier, leurs capteurs de perception. Le travail de cette thèse avait pour objectif d'apporter des solutions permettant d'améliorer la qualité des images sous-marines dans le but de promouvoir l'emploi des robots sous-marins autonomes. La rapidité de calcul est un point très essentiel, car les robots autonomes sont limités par les contraintes d'énergie, la capacité de calcul et de stockage. Dans ce contexte, une méthode rapi
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"Single image haze removal using dark channel prior." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075337.

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But haze removal is highly challenging due to its mathematical ambiguity, typically when the input is merely a single image. In this thesis, we propose a simple but effective image prior, called dark channel prior, to remove haze from a single image. The dark channel prior is a statistical property of outdoor haze-free images: most patches in these images should contain pixels which are dark in at least one color channel. Using this prior with a haze imaging model, we can easily recover high quality haze-free images. Experiments demonstrate that this simple prior is powerful in various situati
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Chen, Po-Fang, and 陳泊芳. "Underwater Image Restoration Based on Dark Channel Prior." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/62952504886353542639.

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碩士<br>國立臺灣大學<br>工程科學及海洋工程學研究所<br>100<br>Underwater images are usually affected by the turbid water medium and floating particles existed in the water. Thus attenuation, absorption, and scattering happen while light propagates in the water. These phenomena cause low contrast in underwater images, and make them look like covering by a veil. In addition, colors disappear sequentially according to the wavelength while light travels deeper in the water, which makes underwater images blue. On the other hand, we observe that underwater images are similar to haze images because they have the same pr
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Lin, Yu-Sheng, and 林育聖. "Adaptive Fast Image Dehazing Algorithms Based on Dark Channel Prior." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/21503323826924481941.

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碩士<br>朝陽科技大學<br>資訊工程系<br>102<br>The quality of digital images is easily affected by imaging conditions. Haze is one of adversarial conditions to degrade the image quality, such as contrast, visibility and color saturation. Many approaches have been proposed to deal with the hazy condition. However, most of them suffer from high computational complexity. In this thesis, several adaptive fast image dehazing algorithms based on the dark channel prior are presented. In the proposed dehazing algorithms, dark channel map is found through 1×1 minimum filter and then used to estimate the atmospheric l
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Lin, Tsu-Fan, and 林簇帆. "Digital Color Image Processing Methods Based on Dark Channel Prior." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/30886370911668099147.

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碩士<br>國立高雄第一科技大學<br>電腦與通訊工程研究所<br>102<br>The dark channel prior (DCP) is a big-data statistics of outdoor haze-free color images. The DCP has been successfully used in the single image haze removal. Due to its success, the purpose of this paper is to develop some color enhancement algorithms based on DCP. These algorithms include a backlighting image enhancement method, a generalized single haze removal method, and an underwater image enhancement method. Several numerical examples and experiments are shown to demonstrate that the proposed image enhancement methods provide higher flexibility th
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Ke, Nai-Cyun, and 柯乃群. "Image/video visibility restoration system based on dark channel prior." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/27035675238152398521.

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碩士<br>國立高雄應用科技大學<br>資訊工程系<br>101<br>We propose a simple and fast algorithm for haze removal in a single input image. During the processing of outdoor images, the presence of haze or smoke reduces the color information of the observed objects. Compare with the existing methods, the proposed method has the advantage of fast processing speed because of the modification of the dark channel prior during the estimation of the transmission. Further, the modification of the parameter reduction of the patch size can avoid the halo effect, which is caused by the improper patch size setting, in the haze-
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Su, Chieh-An, and 蘇玠安. "Unsupervised Image Segmentation Using Sailency Map and Dark Channel Prior." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/hh395n.

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碩士<br>國立清華大學<br>資訊系統與應用研究所<br>105<br>Image saliency detection is a process to pop out the most salient part in the image, and shows up with image saliency map. However, some image saliency maps are not accurate enough to separate foreground and background from images with low contrast; dark channel prior (DCP) can transform these image into a clear image. In this paper, we first apply DCP in image saliency detection to emphasize foreground from image with low contrast saliency. Moreover, we propose a simple cutting method on image saliency. We convert the saliency map into a histogram and use
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Wang, Jian-Ren, and 王建仁. "Nighttime Glow Removal and Low Lighting Image Enhancement Using Dark Channel Prior." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/g5ugaz.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>104<br>Nowadays as people have higher awareness of public safety, different kinds of surveillance technologies emerge. Amongst these surveillance technologies, closed-circuit television (CCTV) and car video recorder are most frequently used by people, who simply want to honestly record the behaviors and acts of other people through images. However, surveillance system highly relies on sufficient light, so surveillance equipment seems ineffective in a low-light environment. But crimes and incidents commonly happen in the nighttime with poor visibility. Therefore, t
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Books on the topic "Dark channel prior"

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Lee, Ho Sang. Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior. BAYSHOP (Generis Publishing), 2023.

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Book chapters on the topic "Dark channel prior"

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John, Jacob, and Prabu Sevugan. "Image Dehazing Through Dark Channel Prior and Color Attenuation Prior." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88244-0_15.

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Shen, Yue, Haoran Zhao, Xin Sun, Yu Zhang, and Junyu Dong. "Underwater Enhancement Model via Reverse Dark Channel Prior." In Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_37.

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Chong, Wenyan, Ying Hu, Defei Yuan, and Yongjun Ma. "Jet Trajectory Recognition Based on Dark Channel Prior." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3476-3_18.

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Saini, Anika, Avinash Sharma, and Kamakshi Rautela. "Single Dark Channel Prior Generalization of Smoggy Image." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9689-6_51.

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Noman, Kahttan A., and Alauldeen Salah Yaseen. "Underwater Image Enhancement by Modified Dark Channel Prior." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68653-5_30.

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Maeda, Koushirou, Keita Hirai, and Takahiko Horiuchi. "Haze Transfer Between Images Based on Dark Channel Prior." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13940-7_17.

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Zhang, Taimei, and Youguang Chen. "Single Image Dehazing Based on Improved Dark Channel Prior." In Advances in Swarm and Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20469-7_23.

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Hu, Junpeng, Xinrong Cao, Xinwei Chen, Zuoyong Li, and Fuquan Zhang. "Modified Image Dehazing Method Based on Dark Channel Prior." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04585-2_19.

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Cai, Zhihao, Shen Zhao, Jiang Zhao, and Yingxun Wang. "Visibility Detection Based on Dark Channel Prior and ResNet." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_521.

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More, Anant R., and S. L. Lahudkar. "Haze Visibility Enhancement of Image Using Dark Channel Prior." In Proceeding of First Doctoral Symposium on Natural Computing Research. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4073-2_45.

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Conference papers on the topic "Dark channel prior"

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He, Jialing, and Liang Zhou. "Fast single image dehazing based on improved dark channel prior." In Second International Conference on Optical Communication and Optical Information Processing (OCOIP 2024), edited by Yang Yue. SPIE, 2025. https://doi.org/10.1117/12.3060988.

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Mushtaq, Zohaib, Obaid Ur Rehman, Usman Ali, and Muhammad A. Latif. "Real-Time Defogging using Dark Channel Prior for Enhanced Visibility." In 2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE). IEEE, 2024. https://doi.org/10.1109/etecte63967.2024.10823762.

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Chowdari, Ch Pratyusha, V. Dheeraj Kashyap, K. Kiran Kumar, M. Raghu Charan, and B. Akhil. "Image Dehazing using Dark Channel Prior." In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917954.

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Kim, Hyeongwoo, Hailin Jin, Sunil Hadap, and Inso Kweon. "Specular Reflection Separation Using Dark Channel Prior." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.192.

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Pan, Jinshan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang. "Blind Image Deblurring Using Dark Channel Prior." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.180.

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Sathya, R., M. Bharathi, and G. Dhivyasri. "Underwater image enhancement by dark channel prior." In 2015 2nd International Conference on Electronics and Communication Systems (ICECS). IEEE, 2015. http://dx.doi.org/10.1109/ecs.2015.7124757.

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Kaur, Jaspreet, Srishti Sabharwal, Ayush Dogra, Bhawna Goyal, and Rohit Anand. "Single Image Dehazing with Dark Channel Prior." In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2021. http://dx.doi.org/10.1109/icrito51393.2021.9596424.

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Liu, ShaSha, and Xianghui Shen. "Image Haze Removal Using Dark Channel Prior." In The International Conference on Computer, Networks and Communication Engineering (ICCNCE 2013). Atlantis Press, 2013. http://dx.doi.org/10.2991/iccnce.2013.66.

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Guo, Tongying, Na Li, and Chao Zhang. "Improved dark channel prior single image defogging." In 2021 33rd Chinese Control and Decision Conference (CCDC). IEEE, 2021. http://dx.doi.org/10.1109/ccdc52312.2021.9601834.

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Yang, Fan, and Yunjie Hu. "Improved Dark Channel Prior for Image Defogging." In 2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT). IEEE, 2022. http://dx.doi.org/10.1109/aicit55386.2022.9930190.

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