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Domain-Aware Unsupervised Hyperspectral Reconstruction for Aerial Image Dehazing [article]

Aditya Mehta, Harsh Sinha, Murari Mandal, Pratik Narang
2020 arXiv   pre-print
The module utilizes task supervision and domain adaptation in order to create a "hyperspectral catalyst" for image dehazing.  ...  SkyGAN consists of 1) a domain-aware hazy-to-hyperspectral (H2H) module, and 2) a conditional GAN (cGAN) based multi-cue image-to-image translation module (I2I) for dehazing.  ...  The HAI dataset is a large-scale dataset for fair evaluation and comparison for aerial image dehazing algorithms.  ... 
arXiv:2011.03677v1 fatcat:t7bsswhllbe6fjltaxdksai7ou

Benchmarking Single Image Dehazing and Beyond [article]

Boyi Li and Wenqi Ren and Dengpan Fu and Dacheng Tao and Dan Feng and Wenjun Zeng and Zhangyang Wang
2019 arXiv   pre-print
We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation  ...  Experiments on RESIDE shed light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.  ...  Changxing Ding, South China University of Technology, for his indispensable support to our data collection and cleaning.  ... 
arXiv:1712.04143v4 fatcat:ncgis7lbufdbrme6a3wsknq65m

A Variational Framework for Single Image Dehazing [chapter]

Adrian Galdran, Javier Vazquez-Corral, David Pardo, Marcelo Bertalmío
2015 Lecture Notes in Computer Science  
We propose to extend a well-known perception-inspired variational framework [1] for the task of single image dehazing.  ...  The main modification consists on the replacement of the value used by this framework for the grey-world hypothesis by an estimation of the mean of the clean image.  ...  In recent years, very powerful algorithms for image dehazing have appeared.  ... 
doi:10.1007/978-3-319-16199-0_18 fatcat:nzv42pjcwbdjnmz4alv6wjrfl4

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

Chuansheng Wang, Jinxing Hu, Xiaowei Luo, Mei-Po Kwan, Weihua Chen, Hao Wang
2022 Sensors  
Experimental results on both the proposed RFSIE and NTIRE'20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality.  ...  Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix  ...  physical imaging model.  ... 
doi:10.3390/s22030911 pmid:35161660 pmcid:PMC8838094 fatcat:5nnsinbx5jfuldvwpidez3guai

An All-in-One Network for Dehazing and Beyond [article]

Boyi Li and Xiulian Peng and Zhangyang Wang and Jizheng Xu and Dan Feng
2017 arXiv   pre-print
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).  ...  Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images.  ...  Prior Work As a prior knowledge to be exploited for dehazing, the hazy image generation follows a well-received physical model (see Section II-A for details).  ... 
arXiv:1707.06543v1 fatcat:3zegktufxvffpli254tr64fppq

Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network

Gu, Zhan, Yuan, Yan
2019 Remote Sensing  
In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal.  ...  Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze.  ...  [10] proposed a physical-based model to describe the appearances of scenery under uniform bad weather conditions and utilizes a quick algorithm to recover the scene contrast.  ... 
doi:10.3390/rs11243008 fatcat:wyvlhh2sk5g57icfsg47vvanx4

Enhanced Variational Image Dehazing

Adrian Galdran, Javier Vazquez-Corral, David Pardo, Marcelo Bertalmío
2015 SIAM Journal of Imaging Sciences  
In this work, we extend a well-known perception-inspired variational framework for single image dehazing. Two main improvements are proposed.  ...  Second, we add a set of new terms to the energy functional for maximizing the inter-channel contrast.  ...  , in which the illumination affecting the scene is non-uniform and the physical model in Eq. (2.1) becomes invalid.  ... 
doi:10.1137/15m1008889 fatcat:axfgft5xjjhnxid6eohvw4ka64

Single image dehazing via combining the prior knowledge and CNNs [article]

Yuwen Li, Chaobing Zheng, Shiqian Wu, Wangming Xu
2021 arXiv   pre-print
Then, the base layer image is passed to the efficient deep convolutional network for estimating the transmission map.  ...  Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo  ...  For example, Fan et al. [6] proposed an image dehazing algorithm * contributed equally.  ... 
arXiv:2111.05701v2 fatcat:gjxzohbpsjd7tj65wh6dsa3jii

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Fayadh Alenezi
2022 Computers Materials & Continua  
We used different images to test the proposed algorithm.  ...  However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.  ...  Atmospheric light and the transmission map are estimated in some dehazing methods through the use of physical maps such as color-attenuation on some non-local priors or through the observation of haze-free  ... 
doi:10.32604/cmc.2022.023339 fatcat:6xnfvpckjvdl3ozdbcvpxonuuu

Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing [article]

Dong Zhao, Jia Li, Hongyu Li, Long Xu
2022 arXiv   pre-print
Extensive experiments on homogeneous, non-homogeneous, and nighttime dehazing tasks reveal that the proposed dehazing network can achieve comparable or even better performance than CNNs-based dehazing  ...  To effectively aggregate these complementary features, we propose a complementary features selection module (CFSM) to select the more useful features for image dehazing.  ...  SOTS Non-Homogeneous Dehazing We further evaluate our method on the non-homogeneous dehazing dataset NTIRE2020-Dehaze. Fig.6 reveal the visual comparisons.  ... 
arXiv:2109.07100v3 fatcat:dnyznvqctbfuhazqb5qnstzhpe

Semantic Single-Image Dehazing [article]

Ziang Cheng, Shaodi You, Viorela Ila, Hongdong Li
2018 arXiv   pre-print
Previous solutions largely rely on handcrafted priors to compensate for this deficiency.  ...  We argue that semantic context can be exploited to give informative cues for (a) learning color prior on clean image and (b) estimating ambient illumination.  ...  Acknowledgement We would like to thank Christos Sakaridis for his careful review of our paper which helped improve its quality.  ... 
arXiv:1804.05624v2 fatcat:dwxhbacfgbc57ngos3zyhw4eoa

DehazeNet: An End-to-End System for Single Image Haze Removal

Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, Dacheng Tao
2016 IEEE Transactions on Image Processing  
Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.  ...  In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation.  ...  Compared with the six algorithms, our results avoid image oversaturation and retain the dehazing validity due to the non-linear regression of DehazeNet. V.  ... 
doi:10.1109/tip.2016.2598681 pmid:28873058 fatcat:twntvgqjtbhupdw5duwzzkkpt4

Dense Scattering Layer Removal [article]

Qiong Yan, Li Xu, Jiaya Jia
2013 arXiv   pre-print
We introduce non-local structure-aware regularization to properly constrain transmission estimation without introducing the halo artifacts.  ...  Instead, we apply optimization to infer a visually plausible L image.  ...  where r h = c (ī c (x) −l c (x)) 3 + λ 6   − h−1 j=0 w j + W −1 j=h w j   , (25) for h = 0, . . . , W . Based on this relaxation scheme, we present the overview of our algorithm in Algorithm 1.  ... 
arXiv:1310.3452v1 fatcat:yemzxa4ijnhgrm45ooz2fmqpwe

An Extensive Literature Review on Underwater Image Colour Correction

Marinos Vlachos, Dimitrios Skarlatos
2021 Sensors  
The criteria for which most of them were designed, as well as the quality evaluation used to measure their effectiveness, are underlined.  ...  dehazing.  ...  The authors then use a typical image dehazing approach to retrieve the whole physical model of the scene after the parameters have been obtained.  ... 
doi:10.3390/s21175690 pmid:34502585 fatcat:e4ikj3lvzrc67gx6wqodlrvzrq

Simultaneous underwater visibility assessment, enhancement and improved stereo

Martin Roser, Matthew Dunbabin, Andreas Geiger
2014 2014 IEEE International Conference on Robotics and Automation (ICRA)  
Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility.  ...  The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model.  ...  Table I illustrates the mean and standard deviation computation times for 640 × 480 pixel images using a single 2.4 GHz CPU running a non-optimized MATLAB TM implementation of the proposed algorithms.  ... 
doi:10.1109/icra.2014.6907416 dblp:conf/icra/RoserDG14 fatcat:qnde2uympjdoximhxjb3jewgdi
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