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DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing [article]

Minghan Fu, Huan Liu, Yankun Yu, Jun Chen, Keyan Wang
2021 arXiv   pre-print
Firstly, due to the complicated haze distribution, texture details are easy to be lost during the dehazing process.  ...  To tackle these two issues, we introduce a novel dehazing network using 2D discrete wavelet transform, namely DW-GAN.  ...  TDN is the winner method in NTIRE 2020 NonHomogeneous Dehazing Challenge. Quantitative Results Comparison. The experiment results are shown in Table. 2.  ... 
arXiv:2104.08911v2 fatcat:7cnprpthpvgjbljrur2ycrml2i

Table of Contents

2021 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Brown (York University) NTIRE 2021 NonHomogeneous Dehazing Challenge Report 627 Codruta O.  ...  Bhat (ETH Zurich), Martin Danelljan (ETH Zurich), and Radu Timofte (ETH Zurich) 647 Ren Yang (NTIRE 2021 challenge) and Radu Timofte (NTIRE 2021 challenge) Single Image Dehazing Using Bounded Channel  ... 
doi:10.1109/cvprw53098.2021.00004 fatcat:yh3zw4afzneeza6jrd6377fd3y

NTIRE 2021 Multi-modal Aerial View Object Classification Challenge [article]

Jerrick Liu, Nathan Inkawhich, Oliver Nina, Radu Timofte, Sahil Jain, Bob Lee, Yuru Duan, Wei Wei, Lei Zhang, Songzheng Xu, Yuxuan Sun, Jiaqi Tang (+22 others)
2022 arXiv   pre-print
In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR.  ...  Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition  ...  This challenge is one of the NTIRE 2021 associated challenges: nonhomogeneous dehazing [4] , defocus deblurring using dual-pixel [3], depth guided image relighting [7] , image deblurring [24] , multi-modal  ... 
arXiv:2107.01189v3 fatcat:td5roq6nh5bkdh4m4suc2a4ruq

DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift [article]

Chia-Ming Chang, Tsung-Nan Lin
2022 arXiv   pre-print
Deep learning-based methods have achieved considerable success on single image dehazing in recent years.  ...  These datasets were introduced in the NTIRE challenge [1, 4, 6] . Since the haze in these datasets is produced by professional haze machines, they are more challenging than synthetic datasets.  ...  By training with translated samples, the dehazing network can achieve better results on the real domain. [25] employs Copy-Blend augmentation [24] to simulate nonhomogeneous haze distributions.  ... 
arXiv:2109.12544v2 fatcat:pdu64gwpbfdireulv7r5wmzibq

NTIRE 2021 Challenge on Perceptual Image Quality Assessment [article]

Jinjin Gu and Haoming Cai and Chao Dong and Jimmy S. Ren and Yu Qiao and Shuhang Gu and Radu Timofte and Manri Cheon and Sungjun Yoon and Byungyeon Kang and Junwoo Lee and Qing Zhang and Haiyang Guo and Yi Bin and Yuqing Hou and Hengliang Luo and Jingyu Guo and Zirui Wang and Hai Wang and Wenming Yang and Qingyan Bai and Shuwei Shi and Weihao Xia and Mingdeng Cao and Jiahao Wang and Yifan Chen and Yujiu Yang and Yang Li and Tao Zhang and Longtao Feng and Yiting Liao and Junlin Li and William Thong and Jose Costa Pereira and Ales Leonardis and Steven McDonagh and Kele Xu and Lehan Yang and Hengxing Cai and Pengfei Sun and Seyed Mehdi Ayyoubzadeh and Ali Royat and Sid Ahmed Fezza and Dounia Hammou and Wassim Hamidouche and Sewoong Ahn and Gwangjin Yoon and Koki Tsubota and Hiroaki Akutsu and Kiyoharu Aizawa
2021 arXiv   pre-print
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR  ...  2021.  ...  Acknowledgements We thank the NTIRE 2021 sponsors: Huawei, Facebook Reality Labs, Wright Brothers Institute, MediaTek, OPPO and ETH Zurich (Computer Vision Lab).  ... 
arXiv:2105.03072v3 fatcat:mf4q3hvz4jbepgyhkzg7wbylqq

NTIRE 2021 Depth Guided Image Relighting Challenge

Majed El Helou, Ruofan Zhou, Sabine Susstrunk, Radu Timofte, Maitreya Suin, A. N. Rajagopalan, Yuanzhi Wang, Tao Lu, Yanduo Zhang, Yuntao Wu, Hao-Hsiang Yang, Wei-Ting Chen (+32 others)
2021 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information.  ...  In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer.  ...  Acknowledgements We thank the NTIRE 2021 sponsors: Huawei, Facebook Reality Labs, Wright Brothers Institute, MediaTek, OPPO and ETH Zurich (Computer Vision Lab).  ... 
doi:10.1109/cvprw53098.2021.00069 fatcat:mihljetrtnggdbtohljudl5ioq

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results [article]

Ren Yang, Radu Timofte, Jing Liu, Yi Xu, Xinjian Zhang, Minyi Zhao, Shuigeng Zhou, Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy, Xin Li (+60 others)
2021 arXiv   pre-print
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.  ...  In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks.  ...  Acknowledgments We thank the NTIRE 2021 sponsors: Huawei, Facebook Reality Labs, Wright Brothers Institute, MediaTek, OPPO and ETH Zurich (Computer Vision Lab).  ... 
arXiv:2104.10781v5 fatcat:v7q6rfewwbdbhfe7lj2i4u2gxa