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Recursive Deep Residual Learning for Single Image Dehazing

Yixin Du, Xin Li
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
a single image by a deep residue learning (DRL) network.  ...  There have been a flurry of works on deep learning based image dehazing in recent years.  ...  Deep Residue Learning (DRL) Network for Image Dehazing In early works of image denoising via deep neural networks, a direct mapping from noise image I(x) to clean image J(x) is learned from the training  ... 
doi:10.1109/cvprw.2018.00116 dblp:conf/cvpr/DuL18 fatcat:qhrdli2a7rbyzp6dxx4aup5iuu

Progressive Depth Learning for Single Image Dehazing [article]

Yudong Liang, Bin Wang, Jiaying Liu, Deyu Li, Sanping Zhou, Wenqi Ren
2021 arXiv   pre-print
In this paper, a deep end-to-end model that iteratively estimates image depths and transmission maps is proposed to perform an effective depth prediction for hazy images and improve the dehazing performance  ...  The image depth and transmission map are progressively refined to better restore the dehazed image.  ...  In this paper, a progressive depth learning approach for single image dehazing is proposed to estimate and refine the depth and transmission map iteratively.  ... 
arXiv:2102.10514v1 fatcat:f22dxkhkszdo5mn3w3ibwqo3eq

A Model-Driven Deep Dehazing Approach by Learning Deep Priors

Dong Yang, Jian Sun
2021 IEEE Access  
Finally, inspired by the optimization algorithm, we design a deep dehazing neural network, dubbed as proximal dehaze-net, by learning the proximal operators for haze-related image priors using CNNs.  ...  Extensive experiments show that our method achieves promising performance for single image dehazing.  ...  CONCLUSION In this paper, we propose a model-driven deep learning approach, proximal dehaze-net, for single image dehazing.  ... 
doi:10.1109/access.2021.3101319 fatcat:zpojrier2rc3hjimijx2idpbw4

Single Image Dehazing: An Analysis on Generative Adversarial Network

Amina Khatun, Mohammad Reduanul Haque, Rabeya Basri, Mohammad Shorif Uddin
2020 Journal of Computer and Communications  
Recently, in addition to the conventional dehazing mechanisms, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance.  ...  To bridge this gap, this presents a comprehensive study on three single image dehazing state-of-the-art GAN models, such as AOD-Net, cGAN, and DHSGAN.  ...  for single image dehazing.  ... 
doi:10.4236/jcc.2020.84010 fatcat:dhfj6wdzbvh43nezknbwkhdkye

Progressive Feature Fusion Network for Realistic Image Dehazing [article]

Kangfu Mei, Aiwen Jiang, Juncheng Li, Mingwen Wang
2018 arXiv   pre-print
Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed.  ...  An U-Net like encoder-decoder deep network via progressive feature fusions has been proposed to directly learn highly nonlinear transformation function from observed hazy image to haze-free ground-truth  ...  Many popular deep learning based image dehazing network can not afford image of such high resolution on a single TITAN X GPU. We owe our advantage to the effective encoder-decoder architecture.  ... 
arXiv:1810.02283v1 fatcat:lszrny4wgredfhgquwmmy3lydq

Perceptually Optimized Generative Adversarial Network for Single Image Dehazing [article]

Yixin Du, Xin Li
2018 arXiv   pre-print
Existing approaches towards single image dehazing including both model-based and learning-based heavily rely on the estimation of so-called transmission maps.  ...  To overcome this weakness, we propose a direct deep learning approach toward image dehazing bypassing the step of transmission map estimation and facilitating end-to-end perceptual optimization.  ...  Based on such formation model, the problem of single image dehazing boils down to estimating the transmission map; and for this reason, many previous works on single image dehazing have focused on a model-based  ... 
arXiv:1805.01084v1 fatcat:mitlvpnbpjgojnxq3braclnvty

Joint Transmission Map Estimation and Dehazing using Deep Networks [article]

He Zhang, Vishwanath Sindagi, Vishal M. Patel
2019 arXiv   pre-print
In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing.  ...  In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned directly from the loss function.  ...  To generate better dehazed image and enable the whole process (estimation of the transmission map and the dehazed image) end-to-end, we propose a deep transmission guided network for single image dehazing  ... 
arXiv:1708.00581v2 fatcat:qihhgqukzjacxlw4l4hxzvd5ny

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.  ...  An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning method.  ...  CONCLUSION A single image dehazing via combining the prior knowledge and deep learning is proposed in this paper.  ... 
arXiv:2111.05701v2 fatcat:gjxzohbpsjd7tj65wh6dsa3jii

A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing [article]

Le-Anh Tran, Seokyong Moon, Dong-Chul Park
2022 arXiv   pre-print
A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for single image dehazing scheme is proposed in this paper.  ...  Experiments on benchmark datasets show that the proposed EDN-GTM outperforms most of traditional and deep learning-based image dehazing schemes in terms of PSNR and SSIM metrics.  ...  Conclusions In this paper, a novel encoder-decoder generative network with guided transmission map (EDN-GTM) for single image dehazing is proposed.  ... 
arXiv:2202.04757v1 fatcat:pnphrhgdqfavnihulxf367ma3i

Single Image Dehazing Using End-to-End Deep-Dehaze Network

Masud An-Nur Islam Fahim, Ho Yub Jung
2021 Electronics  
Unlike other learning-based approaches, our network does not use any fusion connection for image dehazing.  ...  Image dehazing for the observed images is a complicated task because of its ill-posed nature. This study offers the Deep-Dehaze network to retrieve haze-free images.  ...  Result Analysis Many datasets are available for training the deep learning network to perform image dehazing tasks. In our previous work, we used the RESIDE dataset for single image dehazing.  ... 
doi:10.3390/electronics10070817 fatcat:2yalxgp4qbfutof2f4rgyexeju

The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing [article]

Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun
2018 arXiv   pre-print
We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image.  ...  We adopt the convolutional layers of the pre-trained VGG network as encoder to exploit the representation power of deep features, and demonstrate the effectiveness of instance normalization for image dehazing  ...  The superior performance of our network on the benchmark dataset demonstrate the effectiveness of deep networks and instance normalization for single image dehazing.  ... 
arXiv:1805.03305v1 fatcat:22avqamhnzcfvedq3kepoq2yde

Progressive residual learning for single image dehazing [article]

Yudong Liang, Bin Wang, Jiaying Liu, Deyu Li, Yuhua Qian, Wenqi Ren
2021 arXiv   pre-print
In this paper, a progressive residual learning strategy has been proposed to combine the physical model-free dehazing process with reformulated scattering model-based dehazing operations, which enjoys  ...  The proposed method performs favorably against the state-of-the-art methods on public dehazing benchmarks with better model interpretability and adaptivity for complex hazy data.  ...  [7] proposed to learn the residuals between high-resolution and low-resolution images for image super-resolution problem, which largely accelerated the training speed and performances of deep networks  ... 
arXiv:2103.07973v1 fatcat:tomuvkmhnvfzhjm46zdrqhmxxy

Single Image Dehazing with An Independent Detail-Recovery Network [article]

Yan Li, De Cheng, Jiande Sun, Dingwen Zhang, Nannan Wang, Xinbo Gao
2021 arXiv   pre-print
In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates  ...  Besides, we integrate the DRN, the physical-model-based dehazing network and the reconstruction loss into an end-to-end joint learning framework.  ...  ACKNOWLEDGMENT This work is partially supported by the Joint Project for Smart Computing of Shandong Natural Science Foundation (ZR2020LZH015) and the National Natural Science Foundation of China (No.  ... 
arXiv:2109.10492v1 fatcat:brorhgmopvaxlbxqgiel4g3y2i

High-Resolution Representations Network for Single Image Dehazing

Wensheng Han, Hong Zhu, Chenghui Qi, Jingsi Li, Dengyin Zhang
2022 Sensors  
In this paper, we make a simple yet effective modification to the network and apply it to image dehazing. We add a new stage to the original network to make it better for image dehazing.  ...  Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based  ...  Sensors 2022, 22, 2257 Acknowledgments: The authors would like to thank all authors of previous papers for approving the use of their published research results in this paper.  ... 
doi:10.3390/s22062257 pmid:35336428 pmcid:PMC8949864 fatcat:4fogjwtjc5bzfirgavfkyrpy2e

MMP-Net: a Multi-scale Feature Multiple Parallel Fusion Network for Single Image Haze Removal

Jiajia Yan, Chaofeng Li, Yuhui Zheng, Shoukun Xu, Xiaoyong Yan
2020 IEEE Access  
In this paper, combining with atmospheric scattering model, we propose an end-toend multi-scale feature multiple parallel fusion network called MMP-Net for single image haze removal.  ...  What's more, MMP-Net gains the best subjective visual quality on real-world hazy images. INDEX TERMS Image dehazing, convolutional neural network, residual learning, parallel fusion.  ...  A deep residual learning (DRL) network that directly estimates the non-linear mapping of the input image to the output image is designed for dehazing. Mei et al.  ... 
doi:10.1109/access.2020.2971092 fatcat:5iaxnsokfjhjbntux76uomrzqa
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