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 \cite{LF-InterNet}. 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 \cite{DistgLF}. 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.
翻译:在本文中,我们总结了第一个NTIRE光场(LF)图像超分辨率(SR)挑战,旨在在标准的双三次退化下将LF图像超分辨率增强4倍。该挑战开发了一个名为NTIRE-2023的新的LF数据集进行验证和测试,并提供了一个名为BasicLFSR的工具箱,以便于模型开发。与单图像SR相比,LF图像SR的主要挑战在于如何利用大量视角的互补角度信息以及不同视差。总共,有148位参与者注册了这个挑战,其中11个团队成功提交了结果,其PSNR得分高于基线方法LF-InterNet \cite{LF-InterNet}。这些新开发的方法在LF图像SR方面取得了新的最佳效果,例如,获胜的方法在现有最新方法DistgSSR \cite{DistgLF}的基础上实现了约1 dB PSNR的提高。我们报告了参与者提出的解决方案,并总结了他们的共同趋势和有用技巧。我们希望这个挑战可以激发未来研究并激发LF图像SR的新思路。