NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Radu Timofte, Yulan Guo, Kai Jin, Zeqiang Wei, Angulia Yang, Sha Guo, Mingzhi Gao, Xiuzhuang Zhou, Vinh Van Duong, Thuc Nguyen Huu, Jonghoon Yim, Byeungwoo Jeon, Yutong Liu, Zhen Cheng, Zeyu Xiao, Ruikang Xu, Zhiwei Xiong, Gaosheng Liu, Manchang Jin, Huanjing Yue, Jingyu Yang, Chen Gao, Shuo Zhang, Chang Song, Youfang Lin, Wentao Chao, Xuechun Wang, Guanghui Wang, Fuqing Duan, Xia Wang, Yan Wang, Peiqi Xia, Shunzhou Wang, Yao Lu, Ruixuan Cong, Hao Sheng, Da Yang, Rongshan Chen, Sizhe Wang, Zhenglong Cui, Yilei Chen, Yong‐Jie Lu, Dongjun Cai, Ping An, Ahmed Salem, Hatem Ibrahem, Bilel Yagoub, Hyun‐Soo Kang, Zekai Zeng, Wu H
Abstract
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR.