Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Shi, Shuwei"'
Machine learning has been used to identify phase transitions in a variety of physical systems. However, there is still a lack of relevant research on non-Bloch energy braiding in non-Hermitian systems. In this work, we study non-Bloch energy braiding
Externí odkaz:
http://arxiv.org/abs/2408.01141
Diffusion models excel at producing high-quality images; however, scaling to higher resolutions, such as 4K, often results in over-smoothed content, structural distortions, and repetitive patterns. To this end, we introduce ResMaster, a novel, traini
Externí odkaz:
http://arxiv.org/abs/2406.16476
Autor:
Wu, Tianhe, Shi, Shuwei, Cai, Haoming, Cao, Mingdeng, Xiao, Jing, Zheng, Yinqiang, Yang, Yujiu
Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively assess the human perceptual quality of omnidirectional images (ODIs) without relying on pristine-quality image information. It is becoming more significant with the increasing
Externí odkaz:
http://arxiv.org/abs/2305.10983
The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, hidden states
Externí odkaz:
http://arxiv.org/abs/2212.07339
The alignment of adjacent frames is considered an essential operation in video super-resolution (VSR). Advanced VSR models, including the latest VSR Transformers, are generally equipped with well-designed alignment modules. However, the progress of t
Externí odkaz:
http://arxiv.org/abs/2207.08494
Autor:
Lao, Shanshan, Gong, Yuan, Shi, Shuwei, Yang, Sidi, Wu, Tianhe, Wang, Jiahao, Xia, Weihao, Yang, Yujiu
Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality. Unfortunately, there is a performance drop when assessing the distortion images generated by generative adversarial network (GAN) with seemingly realisti
Externí odkaz:
http://arxiv.org/abs/2204.10485
Autor:
Yang, Ren, Timofte, Radu, Zheng, Meisong, Xing, Qunliang, Qiao, Minglang, Xu, Mai, Jiang, Lai, Liu, Huaida, Chen, Ying, Ben, Youcheng, Zhou, Xiao, Fu, Chen, Cheng, Pei, Yu, Gang, Li, Junyi, Wu, Renlong, Zhang, Zhilu, Shang, Wei, Lv, Zhengyao, Chen, Yunjin, Zhou, Mingcai, Ren, Dongwei, Zhang, Kai, Zuo, Wangmeng, Ostyakov, Pavel, Dmitry, Vyal, Soltanayev, Shakarim, Sergey, Chervontsev, Magauiya, Zhussip, Zou, Xueyi, Yan, Youliang, Michelini, Pablo Navarrete, Lu, Yunhua, Zhang, Diankai, Liu, Shaoli, Gao, Si, Wu, Biao, Zheng, Chengjian, Zhang, Xiaofeng, Lu, Kaidi, Wang, Ning, Canh, Thuong Nguyen, Bach, Thong, Wang, Qing, Sun, Xiaopeng, Ma, Haoyu, Zhao, Shijie, Li, Junlin, Xie, Liangbin, Shi, Shuwei, Yang, Yujiu, Wang, Xintao, Gu, Jinjin, Dong, Chao, Shi, Xiaodi, Nian, Chunmei, Jiang, Dong, Lin, Jucai, Xie, Zhihuai, Ye, Mao, Luo, Dengyan, Peng, Liuhan, Chen, Shengjie, Liu, Xin, Wang, Qian, Liang, Boyang, Dong, Hang, Huang, Yuhao, Chen, Kai, Guo, Xingbei, Sun, Yujing, Wu, Huilei, Wei, Pengxu, Huang, Yulin, Chen, Junying, Lee, Ik Hyun, Khowaja, Sunder Ali, Yoon, Jiseok
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge incl
Externí odkaz:
http://arxiv.org/abs/2204.09314
Autor:
Yang, Sidi, Wu, Tianhe, Shi, Shuwei, Lao, Shanshan, Gong, Yuan, Cao, Mingdeng, Wang, Jiahao, Yang, Yujiu
No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores
Externí odkaz:
http://arxiv.org/abs/2204.08958
Autor:
Gu, Jinjin, Cai, Haoming, Dong, Chao, Ren, Jimmy S., Qiao, Yu, Gu, Shuhang, Timofte, Radu, Cheon, Manri, Yoon, Sungjun, Kang, Byungyeon, Lee, Junwoo, Zhang, Qing, Guo, Haiyang, Bin, Yi, Hou, Yuqing, Luo, Hengliang, Guo, Jingyu, Wang, Zirui, Wang, Hai, Yang, Wenming, Bai, Qingyan, Shi, Shuwei, Xia, Weihao, Cao, Mingdeng, Wang, Jiahao, Chen, Yifan, Yang, Yujiu, Li, Yang, Zhang, Tao, Feng, Longtao, Liao, Yiting, Li, Junlin, Thong, William, Pereira, Jose Costa, Leonardis, Ales, McDonagh, Steven, Xu, Kele, Yang, Lehan, Cai, Hengxing, Sun, Pengfei, Ayyoubzadeh, Seyed Mehdi, Royat, Ali, Fezza, Sid Ahmed, Hammou, Dounia, Hamidouche, Wassim, Ahn, Sewoong, Yoon, Gwangjin, Tsubota, Koki, Akutsu, Hiroaki, Aizawa, Kiyoharu
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 techno
Externí odkaz:
http://arxiv.org/abs/2105.03072
Autor:
Shi, Shuwei, Bai, Qingyan, Cao, Mingdeng, Xia, Weihao, Wang, Jiahao, Chen, Yifan, Yang, Yujiu
Image quality assessment (IQA) aims to assess the perceptual quality of images. The outputs of the IQA algorithms are expected to be consistent with human subjective perception. In image restoration and enhancement tasks, images generated by generati
Externí odkaz:
http://arxiv.org/abs/2104.11599