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AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov,Radu Timofte,Zhilu Zhang,Ming Liu,Haolin Wang,Wangmeng Zuo,Jiawei Zhang,Ruimao Zhang,Zhanglin Peng,Sijie Ren,Linhui Dai,Xiaohong Liu,Chengqi Li,Jun Chen,Yuichi Ito,Bhavya Vasudeva,Puneesh Deora,Umapada Pal,Zhenyu Guo,Yu Zhu,Tian Liang,Chenghua Li,Cong Leng,Zhihong Pan,Baopu Li,Byung-Hoon Kim,Joonyoung Song,Jong Chul Ye,JaeHyun Baek,Magauiya Zhussip,Yeskendir Koishekenov,Hwechul Cho Ye,Xin Liu,Xueying Hu,Jun Jiang,Jinwei Gu,Kai Li,Pengliang Tan,Bingxin Hou +38 more
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This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.Abstract:
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to-RGB mapping problem, where to goal was to map the original low-quality RAW images captured by the Huawei P20 device to the same photos obtained with the Canon 5D DSLR camera. The considered task embraced a number of complex computer vision subtasks, such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical image signal processing pipeline modeling.read more
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Efficient Image Super-Resolution Using Pixel Attention
TL;DR: This work designs a lightweight convolutional neural network for image super resolution with a newly proposed pixel attention scheme that could achieve similar performance as the lightweight networks - SRResNet and CARN, but with only 272K parameters.
Proceedings ArticleDOI
NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results
Jianrui Cai,Shuhang Gu,Radu Timofte,Lei Zhang,Xiao Liu,Ding Yukang,Dongliang He,Chao Li,Yi Fu,Shilei Wen,Ruicheng Feng,Jinjin Gu,Yu Qiao,Chao Dong,Dongwon Park,Se Young Chun,Sanghoon Yoon,Junhyung Kwak,Donghee Son,Syed Waqas Zamir,Aditya Arora,Salman H. Khan,Fahad Shahbaz Khan,Ling Shao,Zhengping Wei,Lei Liu,Hong Cai,Darui Li,Fujie Gao,Zheng Hui,Xiumei Wang,Xinbo Gao,Guoan Cheng,Ai Matsune,Qiuyu Li,Leilei Zhu,Huaijuan Zang,Shu Zhan,Yajun Qiu,Ruxin wang,Jiawei Li,Yongcheng Jing,Mingli Song,Pengju Liu,Kai Zhang,Jingdong Liu,Jiye Liu,Hongzhi Zhang,Wangmeng Zuo,Wenyi Tang,Jing Liu,Youngjung Kim,Changyeop Shin,Minbeom Kim,Sungho Kim,Pablo Navarrete Michelini,Hanwen Liu,Dan Zhu,Xuan Xu,Xin Li,Furui Bai,Xiaopeng Sun,Lin Zha,Yuanfei Huang,Wen Lu,Yanpeng Cao,Du Chen,Zewei He,Sun Anshun,Siliang Tang,Fan Hongfei,Xiang Li,Li Guo,Zhang Wenjie,Zhang Yumei,Qingwen He,Jinghui Qin,Lishan Huang,Yukai Shi,Pengxu Wei,Wushao Wen,Liang Lin,Jun Yu,Guochen Xie,Mengyan Li,Rong Chen,Xiaotong Luo,Chen Hong,Yanyun Qu,Cuihua Li,Zhi-Song Liu,Li-Wen Wang,Chu-Tak Li,Can Zhao,Bowen Li,Chung-Chi Tsai,Shang-Chih Chuang,Joon-Hee Choi,Joon-Soo Kim,Xiaoyun Jiang,Ze Pan,Qunbo Lv,Zheng Tan,Peidong He +103 more
TL;DR: The 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) is reviewed with a focus on proposed solutions and results and the state-of-the-art in real-world single image super- resolution.
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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang,Martin Danelljan,Yawei Li,Radu Timofte,Jie Liu,Jie Tang,Gangshan Wu,Yu Zhu,Xiangyu He,Wenjie Xu,Chenghua Li,Cong Leng,Jian Cheng,Guangyang Wu,Wenyi Wang,Xiaohong Liu,Hengyuan Zhao,Xiangtao Kong,Jingwen He,Yu Qiao,Chao Dong,Xiaotong Luo,Liang Chen,Jiangtao Zhang,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Xiaochuan Li,Zhiqiang Lang,Jiangtao Nie,Wei Wei,Lei Zhang,Abdul Muqeet,Jiwon Hwang,Subin Yang,JungHeum Kang,Sung-Ho Bae,Yongwoo Kim,Yanyun Qu,Geun-Woo Jeon,Jun-Ho Choi,Jun-Hyuk Kim,Jong-Seok Lee,Steven Marty,Eric Marty,Dongliang Xiong,Siang Chen,Lin Zha,Jiande Jiang,Xinbo Gao,Wen Lu,Haicheng Wang,Vineeth Bhaskara,Alex Levinshtein,Stavros Tsogkas,Allan D. Jepson,Xiangzhen Kong,Tongtong Zhao,Shanshan Zhao,Hrishikesh P S,Densen Puthussery,C. V. Jiji,Nan Nan,Shuai Liu,Jie Cai,Zibo Meng,Jiaming Ding,Chiu Man Ho,Xuehui Wang,Qiong Yan,Yuzhi Zhao,Long Chen,Long Sun,Wenhao Wang,Zhenbing Liu,Rushi Lan,Rao Muhammad Umer,Christian Micheloni +77 more
TL;DR: The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
Book ChapterDOI
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou,Ruofan Zhou,Sabine Süsstrunk,Radu Timofte,Mahmoud Afifi,Michael S. Brown,Kele Xu,Hengxing Cai,Yuzhong Liu,Li-Wen Wang,Zhi-Song Liu,Chu-Tak Li,Sourya Dipta Das,Nisarg Shah,Akashdeep Jassal,Tongtong Zhao,Shanshan Zhao,Sabari Nathan,M. Parisa Beham,R. Suganya,Qing Wang,Zhongyun Hu,Xin Huang,Yaning Li,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Densen Puthussery,P. S. Hrishikesh,Melvin Kuriakose,C. V. Jiji,Yu Zhu,Liping Dong,Zhuolong Jiang,Chenghua Li,Cong Leng,Jian Cheng +36 more
TL;DR: The AIM 2020 challenge on virtual image relighting and illumination estimation as discussed by the authors focused on one-to-one relighting, where the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation.
Proceedings ArticleDOI
AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results
Shuhang Gu,Hanwen Liu,Dan Zhu,Tangxin Xie,Xin Yang,Chen Zhu,Jia Yu,Wenyu Sun,Xin Tao,Zijun Deng,Liying Lu,Martin Danelljan,Wenbo Li,Taian Guo,Xiaoyong Shen,Xuemiao Xu,Yu-Wing Tai,Jiaya Jia,Peng Yi,Zhongyuan Wang,Kui Jiang,Junjun Jiang,Radu Timofte,Jiayi Ma,Zhi-Song Liu,Li-Wen Wang,Chu-Tak Li,Wan-Chi Siu,Yui-Lam Chan,Ruofan Zhou,Majed Ei Helou,Kuldeep Purohit,Praveen Kandula,Muhammad Haris,Maitreya Suin,Rajagopalan A.N,Kazutoshi Akita,Greg Shakhnarovic,Norimichi Ukita,Pablo Navarrete Michelini,Wenbin Chen +40 more
TL;DR: This paper reviews the extreme video super-resolution challenge from the AIM 2019 workshop, with emphasis on submitted solutions and results.
References
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Adam: A Method for Stochastic Optimization
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TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
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Least Squares Generative Adversarial Networks
TL;DR: The Least Squares Generative Adversarial Network (LSGAN) as discussed by the authors adopts the least square loss function for the discriminator to solve the vanishing gradient problem in GANs.
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Least Squares Generative Adversarial Networks
TL;DR: This paper proposes the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator, and shows that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence.
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Image Super-Resolution Using Very Deep Residual Channel Attention Networks
TL;DR: This work proposes a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections, and proposes a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels.
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