NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results

1 code implementation • 20 Apr 2025 • Zheng Chen, Kai Liu, Jue Gong, Jingkai Wang, Lei Sun, Zongwei Wu, Radu Timofte, Yulun Zhang, Xiangyu Kong, Xiaoxuan Yu, Hyunhee Park, Suejin Han, Hakjae Jeon, Dafeng Zhang, Hyung-Ju Chun, Donghun Ryou, Inju Ha, Bohyung Han, Lu Zhao, Yuyi Zhang, Pengyu Yan, Jiawei Hu, Pengwei Liu, Fengjun Guo, Hongyuan Yu, Pufan Xu, Zhijuan Huang, Shuyuan Cui, Peng Guo, Jiahui Liu, Dongkai Zhang, Heng Zhang, Huiyuan Fu, Huadong Ma, Yanhui Guo, Sisi Tian, Xin Liu, Jinwen Liang, Jie Liu, Jie Tang, Gangshan Wu, Zeyu Xiao, Zhuoyuan Li, Yinxiang Zhang, Wenxuan Cai, Vijayalaxmi Ashok Aralikatti, Nikhil Akalwadi, G Gyaneshwar Rao, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudenagudi, Marcos V. Conde, Alejandro Merino, Bruno Longarela, Javier Abad, Weijun Yuan, Zhan Li, Zhanglu Chen, Boyang Yao, Aagam Jain, Milan Kumar Singh, Ankit Kumar, Shubh Kawa, Divyavardhan Singh, Anjali Sarvaiya, Kishor Upla, Raghavendra Ramachandra, Chia-Ming Lee, Yu-Fan Lin, Chih-Chung Hsu, Risheek V Hiremath, Yashaswini Palani, YuXuan Jiang, Qiang Zhu, Siyue Teng, Fan Zhang, Shuyuan Zhu, Bing Zeng, David Bull, Jingwei Liao, Yuqing Yang, Wenda Shao, Junyi Zhao, Qisheng Xu, Kele Xu, Sunder Ali Khowaja, Ik Hyun Lee, Snehal Singh Tomar, Rajarshi Ray, Klaus Mueller, Sachin Chaudhary, Surya Vashisth, Akshay Dudhane, Praful Hambarde, Satya Naryan Tazi, Prashant Patil, Santosh Kumar Vipparthi, Subrahmanyam Murala, Bilel Benjdira, Anas M. Ali, Wadii Boulila, Zahra Moammeri, Ahmad Mahmoudi-Aznaveh, Ali Karbasi, Hossein Motamednia, Liangyan Li, Guanhua Zhao, Kevin Le, Yimo Ning, Haoxuan Huang, Jun Chen

This paper presents the NTIRE 2025 image super-resolution ($\times$4) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025.

Image Super-Resolution valid

ChainStream: An LLM-based Framework for Unified Synthetic Sensing

1 code implementation • 13 Dec 2024 • Jiacheng Liu, Yuanchun Li, Liangyan Li, Yi Sun, Hao Wen, Xiangyu Li, Yao Guo, Yunxin Liu

Many applications demand context sensing to offer personalized and timely services.

Code Generation

Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization

no code implementations • 4 Sep 2024 • Li Xie, Liangyan Li, Jun Chen, Lei Yu, Zhongshan Zhang

This paper investigates the best known bounds on the quadratic Gaussian distortion-rate-perception function with limited common randomness for the Kullback-Leibler divergence-based perception measure, as well as their counterparts for the squared Wasserstein-2 distance-based perception measure, recently established by Xie et al.

Quantization

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

1 code implementation • 24 Apr 2024 • Marcos V. Conde, Florin-Alexandru Vasluianu, Radu Timofte, Jianxing Zhang, Jia Li, Fan Wang, Xiaopeng Li, Zikun Liu, Hyunhee Park, Sejun Song, Changho Kim, Zhijuan Huang, Hongyuan Yu, Cheng Wan, Wending Xiang, Jiamin Lin, Hang Zhong, Qiaosong Zhang, Yue Sun, Xuanwu Yin, Kunlong Zuo, Senyan Xu, Siyuan Jiang, Zhijing Sun, Jiaying Zhu, Liangyan Li, Ke Chen, Yunzhe Li, Yimo Ning, Guanhua Zhao, Jun Chen, Jinyang Yu, Kele Xu, Qisheng Xu, Yong Dou

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results.

Image Super-Resolution Survey

Output-Constrained Lossy Source Coding With Application to Rate-Distortion-Perception Theory

no code implementations • 21 Mar 2024 • Li Xie, Liangyan Li, Jun Chen, Zhongshan Zhang

The distortion-rate function of output-constrained lossy source coding with limited common randomness is analyzed for the special case of squared error distortion measure.

SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge

no code implementations • 25 Apr 2023 • Ke Chen, Liangyan Li, Huan Liu, Yunzhe Li, Congling Tang, Jun Chen

Stereo Image Super-Resolution (stereoSR) has attracted significant attention in recent years due to the extensive deployment of dual cameras in mobile phones, autonomous vehicles and robots.

Autonomous Vehicles Image Restoration +1

A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer

1 code implementation • 16 Apr 2023 • Yangyi Liu, Huan Liu, Liangyan Li, Zijun Wu, Jun Chen

Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one.

Image Dehazing

Towards Multi-Domain Single Image Dehazing via Test-Time Training

no code implementations • CVPR 2022 • Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, Keyan Wang

Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest.

Image Dehazing Meta-Learning +1

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

1 code implementation • 21 Apr 2021 • 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, Fanglong Liu, He Zheng, Lielin Jiang, Qi Zhang, Dongliang He, Fu Li, Qingqing Dang, Yibin Huang, Matteo Maggioni, Zhongqian Fu, Shuai Xiao, Cheng Li, Thomas Tanay, Fenglong Song, Wentao Chao, Qiang Guo, Yan Liu, Jiang Li, Xiaochao Qu, Dewang Hou, Jiayu Yang, Lyn Jiang, Di You, Zhenyu Zhang, Chong Mou, Iaroslav Koshelev, Pavel Ostyakov, Andrey Somov, Jia Hao, Xueyi Zou, Shijie Zhao, Xiaopeng Sun, Yiting Liao, Yuanzhi Zhang, Qing Wang, Gen Zhan, Mengxi Guo, Junlin Li, Ming Lu, Zhan Ma, Pablo Navarrete Michelini, Hai Wang, Yiyun Chen, Jingyu Guo, Liliang Zhang, Wenming Yang, Sijung Kim, Syehoon Oh, Yucong Wang, Minjie Cai, Wei Hao, Kangdi Shi, Liangyan Li, Jun Chen, Wei Gao, Wang Liu, XiaoYu Zhang, Linjie Zhou, Sixin Lin, Ru Wang

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.