The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report

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 https://github.com/Amazingren/NTIRE2024_ESR/.

Unlocking Low-Light-Rainy Image Restoration by Pairwise Degradation Feature Vector Guidance

Rain in the dark is a common natural phenomenon. Photos captured in such a condition significantly impact the performance of various nighttime activities, such as autonomous driving, surveillance systems, and night photography. While existing methods designed for low-light enhancement or deraining show promising performance, they have limitations in simultaneously addressing the task of brightening low light and removing rain. Furthermore, using a cascade approach, such as ``deraining followed by low-light enhancement'' or vice versa, may lead to difficult-to-handle rain patterns or excessively blurred and overexposed images. To overcome these limitations, we propose an end-to-end network called $L^{2}RIRNet$ which can jointly handle low-light enhancement and deraining. Our network mainly includes a Pairwise Degradation Feature Vector Extraction Network (P-Net) and a Restoration Network (R-Net). P-Net can learn degradation feature vectors on the dark and light areas separately, using contrastive learning to guide the image restoration process. The R-Net is responsible for restoring the image. We also introduce an effective Fast Fourier - ResNet Detail Guidance Module (FFR-DG) that initially guides image restoration using detail image that do not contain degradation information but focus on texture detail information. Additionally, we contribute a dataset containing synthetic and real-world low-light-rainy images. Extensive experiments demonstrate that our $L^{2}RIRNet$ outperforms existing methods in both synthetic and complex real-world scenarios.