Abstract
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at this https URL.
Abstract (translated)
本论文对NTIRE 2024挑战进行了全面的回顾，重点关注高效的单图像超分辨率（ESR）解决方案及其效果。挑战的任务是根据低分辨率和高分辨率图像的成对，将输入图像进行4倍放大的超分辨率。主要目标是在保持DIV2K_LSDIR_valid数据集上的峰值信号-噪声比（PSNR）约为26.90 dB和DIV2K_LSDIR_test数据集上的峰值信号-噪声比（PSNR）约为26.99 dB的同时，开发网络优化各种方面，如运行时间、参数和FLOPs。此外，该挑战分为4个轨道，包括主轨道（总体性能）、子轨道1（运行时间）、子轨道2（FLOPs）和子轨道3（参数）。在主轨道中，考虑了所有三个指标（即运行时间、FLOPs和参数计数）。主轨道的排名基于所有其他子轨道评分之加权求和。在子轨道1中，对提交的实现进行了实际运行时间的评估，并使用相应的得分来确定排名。在子轨道2中，考虑了FLOPs的数量。基于相应的FLOPs计算的得分用于确定排名。在子轨道3中，考虑了参数的数量。基于相应的参数计算的得分用于确定排名。RLFN被设定为效率测量的基准。挑战有262名注册参与者，34支队伍提交了有效的解决方案。它们衡量了ESR的现有水平。为了促进挑战的重复性，并使其他研究人员能够基于这些发现进行构建，代码和预训练模型的知识产权已公开在https://这个URL上。
URL
https://arxiv.org/abs/2404.10343
