Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Conde, Marcos"'
Multiple low-vision tasks such as denoising, deblurring and super-resolution depart from RGB images and further reduce the degradations, improving the quality. However, modeling the degradations in the sRGB domain is complicated because of the Image
Externí odkaz:
http://arxiv.org/abs/2409.18204
Autor:
Conde, Marcos V, Lei, Zhijun, Li, Wen, Bampis, Christos, Katsavounidis, Ioannis, Timofte, Radu
Video super-resolution (VSR) is a critical task for enhancing low-bitrate and low-resolution videos, particularly in streaming applications. While numerous solutions have been developed, they often suffer from high computational demands, resulting in
Externí odkaz:
http://arxiv.org/abs/2409.17256
Implicit Neural Representations (INRs) are a novel paradigm for signal representation that have attracted considerable interest for image compression. INRs offer unprecedented advantages in signal resolution and memory efficiency, enabling new possib
Externí odkaz:
http://arxiv.org/abs/2409.17134
Autor:
Conde, Marcos V., Vasluianu, Florin-Alexandru, Xiong, Jinhui, Ye, Wei, Ranjan, Rakesh, Timofte, Radu
The increasing demand for augmented reality (AR) and virtual reality (VR) applications highlights the need for efficient depth information processing. Depth maps, essential for rendering realistic scenes and supporting advanced functionalities, are t
Externí odkaz:
http://arxiv.org/abs/2409.16277
Autor:
Hosu, Vlad, Conde, Marcos V., Agnolucci, Lorenzo, Barman, Nabajeet, Zadtootaghaj, Saman, Timofte, Radu
We introduce the AIM 2024 UHD-IQA Challenge, a competition to advance the No-Reference Image Quality Assessment (NR-IQA) task for modern, high-resolution photos. The challenge is based on the recently released UHD-IQA Benchmark Database, which compri
Externí odkaz:
http://arxiv.org/abs/2409.16271
Implicit Neural Representations (INRs) and Neural Fields are a novel paradigm for signal representation, from images and audio to 3D scenes and videos. The fundamental idea is to represent a signal as a continuous and differentiable neural network. T
Externí odkaz:
http://arxiv.org/abs/2405.16807
Autor:
Conde, Marcos V., Lei, Zhijun, Li, Wen, Stejerean, Cosmin, Katsavounidis, Ioannis, Timofte, Radu, Yoon, Kihwan, Gankhuyag, Ganzorig, Lv, Jiangtao, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Li, Zhiyuan, Wei, Hao, Ge, Chenyang, Zhang, Dongyang, Liu, Tianle, Chen, Huaian, Jin, Yi, Zhou, Menghan, Yan, Yiqiang, Gao, Si, Wu, Biao, Liu, Shaoli, Zheng, Chengjian, Zhang, Diankai, Wang, Ning, Qiu, Xintao, Zhou, Yuanbo, Wu, Kongxian, Dai, Xinwei, Tang, Hui, Deng, Wei, Gao, Qingquan, Tong, Tong, Lee, Jae-Hyeon, Choi, Ui-Jin, Yan, Min, Liu, Xin, Wang, Qian, Ye, Xiaoqian, Du, Zhan, Zhang, Tiansen, Peng, Long, Guo, Jiaming, Di, Xin, Liao, Bohao, Du, Zhibo, Xia, Peize, Pei, Renjing, Wang, Yang, Cao, Yang, Zha, Zhengjun, Han, Bingnan, Yu, Hongyuan, Wu, Zhuoyuan, Wan, Cheng, Liu, Yuqing, Yu, Haodong, Li, Jizhe, Huang, Zhijuan, Huang, Yuan, Zou, Yajun, Guan, Xianyu, Jia, Qi, Zhang, Heng, Yin, Xuanwu, Zuo, Kunlong, Moon, Hyeon-Cheol, Jeong, Tae-hyun, Yang, Yoonmo, Kim, Jae-Gon, Jeong, Jinwoo, Kim, Sunjei
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a dive
Externí odkaz:
http://arxiv.org/abs/2404.16484
Autor:
Liu, Xiaohong, Min, Xiongkuo, Zhai, Guangtao, Li, Chunyi, Kou, Tengchuan, Sun, Wei, Wu, Haoning, Gao, Yixuan, Cao, Yuqin, Zhang, Zicheng, Wu, Xiele, Timofte, Radu, Peng, Fei, Fu, Huiyuan, Ming, Anlong, Wang, Chuanming, Ma, Huadong, He, Shuai, Dou, Zifei, Chen, Shu, Zhang, Huacong, Xie, Haiyi, Wang, Chengwei, Chen, Baoying, Zeng, Jishen, Yang, Jianquan, Wang, Weigang, Fang, Xi, Lv, Xiaoxin, Yan, Jun, Zhi, Tianwu, Zhang, Yabin, Li, Yaohui, Li, Yang, Xu, Jingwen, Liu, Jianzhao, Liao, Yiting, Li, Junlin, Yu, Zihao, Lu, Yiting, Li, Xin, Motamednia, Hossein, Hosseini-Benvidi, S. Farhad, Guan, Fengbin, Mahmoudi-Aznaveh, Ahmad, Mansouri, Azadeh, Gankhuyag, Ganzorig, Yoon, Kihwan, Xu, Yifang, Fan, Haotian, Kong, Fangyuan, Zhao, Shiling, Dong, Weifeng, Yin, Haibing, Zhu, Li, Wang, Zhiling, Huang, Bingchen, Saha, Avinab, Mishra, Sandeep, Gupta, Shashank, Sureddi, Rajesh, Saha, Oindrila, Celona, Luigi, Bianco, Simone, Napoletano, Paolo, Schettini, Raimondo, Yang, Junfeng, Fu, Jing, Zhang, Wei, Cao, Wenzhi, Liu, Limei, Peng, Han, Yuan, Weijun, Li, Zhan, Cheng, Yihang, Deng, Yifan, Li, Haohui, Qu, Bowen, Li, Yao, Luo, Shuqing, Wang, Shunzhou, Gao, Wei, Lu, Zihao, Conde, Marcos V., Wang, Xinrui, Chen, Zhibo, Liao, Ruling, Ye, Yan, Wang, Qiulin, Li, Bing, Zhou, Zhaokun, Geng, Miao, Chen, Rui, Tao, Xin, Liang, Xiaoyu, Sun, Shangkun, Ma, Xingyuan, Li, Jiaze, Yang, Mengduo, Xu, Haoran, Zhou, Jie, Zhu, Shiding, Yu, Bohan, Chen, Pengfei, Xu, Xinrui, Shen, Jiabin, Duan, Zhichao, Asadi, Erfan, Liu, Jiahe, Yan, Qi, Qu, Youran, Zeng, Xiaohui, Wang, Lele, Liao, Renjie
This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major
Externí odkaz:
http://arxiv.org/abs/2404.16687
Autor:
Conde, Marcos V., Vasluianu, Florin-Alexandru, Timofte, Radu, Zhang, Jianxing, Li, Jia, Wang, Fan, Li, Xiaopeng, Liu, Zikun, Park, Hyunhee, Song, Sejun, Kim, Changho, Huang, Zhijuan, Yu, Hongyuan, Wan, Cheng, Xiang, Wending, Lin, Jiamin, Zhong, Hang, Zhang, Qiaosong, Sun, Yue, Yin, Xuanwu, Zuo, Kunlong, Xu, Senyan, Jiang, Siyuan, Sun, Zhijing, Zhu, Jiaying, Li, Liangyan, Chen, Ke, Li, Yunzhe, Ning, Yimo, Zhao, Guanhua, Chen, Jun, Yu, Jinyang, Xu, Kele, Xu, Qisheng, Dou, Yong
This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem
Externí odkaz:
http://arxiv.org/abs/2404.16223
Autor:
Conde, Marcos V., Zadtootaghaj, Saman, Barman, Nabajeet, Timofte, Radu, He, Chenlong, Zheng, Qi, Zhu, Ruoxi, Tu, Zhengzhong, Wang, Haiqiang, Chen, Xiangguang, Meng, Wenhui, Pan, Xiang, Shi, Huiying, Zhu, Han, Xu, Xiaozhong, Sun, Lei, Chen, Zhenzhong, Liu, Shan, Zhang, Zicheng, Wu, Haoning, Zhou, Yingjie, Li, Chunyi, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao, Sun, Wei, Cao, Yuqin, Jiang, Yanwei, Jia, Jun, Zhang, Zhichao, Chen, Zijian, Zhang, Weixia, Min, Xiongkuo, Göring, Steve, Qi, Zihao, Feng, Chen
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user
Externí odkaz:
http://arxiv.org/abs/2404.16205