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16 results on '"Haoming Cai"'

4. NTIRE 2022 Challenge on Perceptual Image Quality Assessment.

Author

Jinjin Gu, Haoming Cai, Chao Dong 0005, Jimmy S. Ren, Radu Timofte, Yuan Gong 0002, Shanshan Lao, Shuwei Shi, Jiahao Wang, Sidi Yang, Tianhe Wu, Weihao Xia, Yujiu Yang, Mingdeng Cao, Cong Heng, Lingzhi Fu, Rongyu Zhang, Yusheng Zhang, Hao Wang 0073, Hongjian Song, Jing Wang, Haotian Fan, Xiaoxia Hou, Ming Sun, Mading Li, Kai Zhao, Kun Yuan, Zishang Kong, Mingda Wu, Chuanchuan Zheng, Marcos V. Conde, Maxime Burchi, Longtao Feng, Tao Zhang 0042, Yang Li 0041, Jingwen Xu, Haiqiang Wang, Yiting Liao, Junlin Li, Kele Xu, Tao Sun, Yunsheng Xiong, Abhisek Keshari, Komal, Sadbhawana Thakur, Vinit Jakhetiya, Badri N. Subudhi, Hao-Hsiang Yang, Hua-En Chang, Zhi-Kai Huang, Wei-Ting Chen, Sy-Yen Kuo, Saikat Dutta 0002, Sourya Dipta Das, Nisarg A. Shah, and Anil Kumar Tiwari

Published
2022
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6. NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results.

Author

Yawei Li 0001, Kai Zhang 0008, Radu Timofte, Luc Van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu 0001, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen 0006, Haoming Cai, Yu Qiao 0001, Chao Dong 0005, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua 0001, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang 0001, Yuan Xie 0006, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu 0017, Youwei Li, Haoqiang Fan, Jian Sun 0001, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li 0008, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu 0040, Chao Chen, Yidong Cai, Jie Tang 0006, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu 0014, Keyan Wang, Jun Chen 0005, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoglu, Wei Xiong 0008, Chengyi Xiong, Fei Wang, Hao Li 0030, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye 0001, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman H. Khan 0001, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gao, Dengwen Zhou, Qian Ning, Jingzhu Tang, Han Huang, Yufei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li 0005, Jun Liu 0071, Wanjun Wang, Kun Zeng, Hanjiang Lin, Xinyu Chen, and Jinsheng Fang

Published
2022
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15. NTIRE 2021 Challenge on Perceptual Image Quality Assessment

Author

Wassim Hamidouche, Byungyeon Kang, Shuwei Shi, Yujiu Yang, Junlin Li, Pengfei Sun, Jose Costa Pereira, Kele Xu, Hiroaki Akutsu, Hai Wang, Koki Tsubota, Yiting Liao, William Thong, Hengliang Luo, Longtao Feng, Jingyu Guo, Yuqing Hou, Yu Qiao, Sung-Jun Yoon, Jimmy Ren, Tao Zhang, Yang Li, Bin Yi, Yifan Chen, Jinjin Gu, Ali Royat, Steven McDonagh, Shuhang Gu, Junwoo Lee, Kiyoharu Aizawa, Lehan Yang, Sewoong Ahn, Weihao Xia, Qingyan Bai, Haiyang Guo, Zirui Wang, Mingdeng Cao, Qing Zhang, Jiahao Wang, Wenming Yang, Sid Ahmed Fezza, Haoming Cai, Dounia Hammou, Ales Leonardis, Radu Timofte, Hengxing Cai, Manri Cheon, Seyed Mehdi Ayyoubzadeh, Chao Dong, and Gwangjin Yoon

Subjects
FOS: Computer and information sciences, business.industry, Computer science, Image quality, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, Distortion (optics), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image processing, Electrical Engineering and Systems Science - Image and Video Processing, Visualization, FOS: Electrical engineering, electronic engineering, information engineering, Quality (business), Perceptual image quality, Computer vision, Artificial intelligence, business, Perceptual image, Image restoration, media_common
Abstract

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. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.

Published
2021
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16. Toward Interactive Modulation for Photo-Realistic Image Restoration

Author

Jingwen He, Chao Dong, Haoming Cai, and Yu Qiao

Subjects
FOS: Computer and information sciences, Discriminator, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Base (topology), Image (mathematics), Modulation, Pattern recognition (psychology), FOS: Electrical engineering, electronic engineering, information engineering, Artificial intelligence, Tweaking, business, Algorithm, Image restoration, Generator (mathematics)
Abstract

Modulating image restoration level aims to generate a restored image by altering a factor that represents the restoration strength. Previous works mainly focused on optimizing the mean squared reconstruction error, which brings high reconstruction accuracy but lacks finer texture details. This paper presents a Controllable Unet Generative Adversarial Network (CUGAN) to generate high-frequency textures in the modulation tasks. CUGAN consists of two modules -- base networks and condition networks. The base networks comprise a generator and a discriminator. In the generator, we realize the interactive control of restoration levels by tuning the weights of different features from different scales in the Unet architecture. Moreover, we adaptively modulate the intermediate features in the discriminator according to the severity of degradations. The condition networks accept the condition vector (encoded degradation information) as input, then generate modulation parameters for both the generator and the discriminator. During testing, users can control the output effects by tweaking the condition vector. We also provide a smooth transition between GAN and MSE effects by a simple transition method. Extensive experiments demonstrate that the proposed CUGAN achieves excellent performance on image restoration modulation tasks.

Published
2021

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