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NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results

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arxiv 2404.09790 v2 pith:SAAJX4Q5 submitted 2024-04-15 cs.CV

Zheng Chen , Zongwei Wu , Eduard Zamfir , Kai Zhang , Yulun Zhang , Radu Timofte , Xiaokang Yang , Hongyuan Yu
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Cheng Wan Yuxin Hong Zhijuan Huang Yajun Zou Yuan Huang Jiamin Lin Bingnan Han Xianyu Guan Yongsheng Yu Daoan Zhang Xuanwu Yin Kunlong Zuo Jinhua Hao Kai Zhao Kun Yuan Ming Sun Chao Zhou Hongyu An Xinfeng Zhang Zhiyuan Song Ziyue Dong Qing Zhao Xiaogang Xu Pengxu Wei Zhi-chao Dou Gui-ling Wang Chih-Chung Hsu Chia-Ming Lee Yi-Shiuan Chou Cansu Korkmaz A. Murat Tekalp Yubin Wei Xiaole Yan Binren Li Haonan Chen Siqi Zhang Sihan Chen Amogh Joshi Nikhil Akalwadi Sampada Malagi Palani Yashaswini Chaitra Desai Ramesh Ashok Tabib Ujwala Patil Uma Mudenagudi Anjali Sarvaiya Pooja Choksy Jagrit Joshi Shubh Kawa Kishor Upla Sushrut Patwardhan Raghavendra Ramachandra Sadat Hossain Geongi Park S. M. Nadim Uddin Hao Xu Yanhui Guo Aman Urumbekov Xingzhuo Yan Wei Hao Minghan Fu Isaac Orais Samuel Smith Ying Liu Wangwang Jia Qisheng Xu Kele Xu Weijun Yuan Zhan Li Wenqin Kuang Ruijin Guan Ruting Deng Zhao Zhang Bo Wang Suiyi Zhao Yan Luo Yanyan Wei Asif Hussain Khan Christian Micheloni Niki Martinel
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classification cs.CV
keywords challengeperformanceimageimagesntiresolutionssuper-resolutionadvanced
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field.

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  1. ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing

    eess.IV 2025-06 conditional novelty 5.0 of 10

    Under causal low-delay video conferencing SR, single-image baselines won the general and talking-head tracks, OCR-based text recovery won the screen-content track, and PSNR/SSIM correlated weakly with subjective quality.

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