Results 141 to 150 of about 4,947 (184)

NTIRE 2019 Image Dehazing Challenge Report

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019
This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze
Ancuti C. O., Ancuti C., Timofte R., Van Gool L., Zhang L., Yang M. -H., Guo T., Li X., Cherukuri V., Monga V., Jiang H., Yang S., Liu Y., Qu X., Wan P., Park D., Chun S. Y., Hong M., Huang J., Chen Y., Chen S., Wang B., Michelini P. N., Liu H., Zhu D., Liu J., Santra S., Mondal R., Chanda B., Morales P., Klinghoffer T., Quan L. M., Kim Y. -G., Liang X., Li R., Pan J., Tang J., Purohit K., Suin M., Rajagopalan A. N., Schettini R., Bianco S., Piccoli F., Cusano C., Celona L., Hwang S., Ma Y. S., Byun H., Murala S., Dudhane A., Aulakh H., Zheng T., Zhang T., Qin W., Zhou R., Wang S., Tarel J. -P., Wang C., Wu J.   +58 more
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Non-local Image Dehazing

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Haze limits visibility and reduces image contrast in outdoor images. The degradation is different for every pixel and depends on the distance of the scene point from the camera. This dependency is expressed in the transmission coefficients, that control the scene attenuation and amount of haze in every pixel.
Dana Berman, Tali Treibitz, Shai Avidan
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Color Shifting-Aware Image Dehazing

2019 IEEE International Symposium on Multimedia (ISM), 2019
Built upon an image formation model for a single hazy image, existing image dehazing methods typically restore hazed pixels by estimating the unknown transmission map and global ambient light via exploiting image priors. They often produce visually unpleasing results when hazy images are with unwanted color shifts due to inaccurate estimation about the
Jia-Li Yin, Bo-Hao Chen, Yan-Tsung Peng, Yu-Cheng Lin   +3 more
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Contrast-based stereoscopic images dehazing

2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), 2015
As human eyes perceive scenes with slightly different angles, fog effect is referred to the function of the distance between camera and objects. In this paper, a novel contrast-based dehazing algorithm is proposed by using stereoscopic images. The proposed algorithm first decomposes the disparity map in a given fog-and-haze stereo pair with digital ...
Yimin Qiu, Shiqian Wu
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Development of image dehazing system

2016 5th International Conference on Wireless Networks and Embedded Systems (WECON), 2016
The proposed work presents a novel method that comprises process of restoring the foggy images and idea about its hardware implementation. Foggy conditions are one of major major source of accidents for vehicles. Use of proposed a novel “mean channel guided algorithm for defogging is presented whose function is more accurate and robust as compared with
Amruta Deshmukh, Satbir Singh
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Single Smog Image Dehazing Method

2016 3rd International Conference on Information Science and Control Engineering (ICISCE), 2016
Images in fog or smog degrade dreadfully, although there were quite a few image dehazing methods, which were not very effective to smog images. This paper proposed a novel dehazing method based on propagating deconvolution and dark-channel prior. Propagating deconvolution aimed at recovering smog image to get rid of the smog in front of the scene, it ...
Rui Wang, Guoyu Wang
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Single Image Dehazing via Image Generating

2018
Outdoor images taken in bad weather conditions often suffer from poor visibility. However, single image haze removal is an ill-posed problem, because the number of the equations is smaller than the number of unknowns. In this paper, a deep learning-based method, called Dehaze CNN, is proposed to estimate a clear image patch from a hazy image patch ...
Shengdong Zhang, Jian Yao, Edel B. Garcia   +2 more
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Semi-selective image dehazing

2021 33rd Chinese Control and Decision Conference (CCDC), 2021
Yin Gao, Huiqin Xu, Feng Xie, Jun Li
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Mutually Guided Image Dehazing

2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC), 2022
Usman Ali, Waqas Tariq Toor
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UCL-Dehaze: Toward Real-World Image Dehazing via Unsupervised Contrastive Learning

IEEE Transactions on Image Processing
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy ...
Yongzhen, Wang, Xuefeng, Yan, Fu Lee, Wang, Haoran, Xie, Wenhan, Yang, Xiao-Ping, Zhang, Jing, Qin, Mingqiang, Wei   +7 more
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