Jiyu Jin

Nighttime image deraining presents unique challenges compared to daytime scenarios, primarily due to the complex characteristics of nighttime scenes and the limited availability of datasets that effectively capture the interplay between lighting and rain streaks. In this paper, we contribute a high-quality nighttime image deraining dataset, BDD-NightRain, consisting of 5,600 synthetic nighttime rain images with higher realism and harmony for driving scenes. Furthermore, we develop a two-stage Nighttime Rain Removal Network (NRRNet) that leverages color space transformation. Specifically, the coarse processing stage focuses on rain streak removal in the Y channel, where luminance contrast is most pronounced under nighttime rain conditions, while the fine processing stage further refines the deraining results of the previous stage. In addition, we introduce coordinate-based implicit neural representations between these two stages to guide the model's focus toward areas with appropriate illumination. Extensive experiments show that our method outperforms others in removing rain streaks from nighttime scenes, with potential applications in night time object detection. The code and dataset will be available at https://github.com/guanqiyuan/NRRNet.

Single image deraining aims to recover clean background scenes from raincontaminated images. Existing methods often enlarge the receptive field tomodel long-range spatial dependencies and better capture rain streak patterns. However, many of them rely on Transformer-based self-attention orconventional large-kernel convolutions, whose computational and parametercosts grow rapidly with the receptive field, limiting efficiency and practicality. To address this issue, we propose a Multi-Scale Large Kernel AttentionNetwork for efficient and high-quality single image deraining. Specifically,we design a Large Kernel Attention Block that combines strip convolutionsand dilated convolutions within an attention framework, enabling near-globalcontext aggregation with low computational overhead. To alleviate detaildistortion caused by long-range modeling, we further introduce a Multi-scaleProgressive Detail Recovery module, which progressively fuses cross-scalefeatures and suppresses residual degradation for fine-detail restoration. Inaddition, an Edge Recovery Block is incorporated to enhance horizontal andvertical structural cues, improving edge consistency and overall structuralfidelity. Extensive experiments on both synthetic and real-world benchmarkdatasets demonstrate that MLKANet achieves competitive deraining performance while maintaining favorable efficiency.

Single image deraining aims to restore clean background scenes from rainy images. To address the high computational and parameter costs caused by large receptive field modeling in existing methods, we propose a multi-scale large kernel attention network for efficient and high-quality deraining. Specifically, a large kernel attention block is designed by combining strip convolutions and dilated convolutions within an attention framework, enabling near-global context aggregation with low computational overhead. To alleviate detail distortion introduced by long-range modeling, a multi-scale progressive detail recovery module is further developed to gradually fuse cross-scale features and suppress residual degradations. In addition, an edge-aware reconstruction module is incorporated to enhance horizontal and vertical structural information, improving edge consistency and overall structural integrity. Experimental results show that the proposed method achieves competitive performance on both synthetic and real-world benchmark datasets.

An algorithm for sinusoidal frequency estimation is proposed, which relies on interpolating discrete time Fourier transform (DTFT) spectral lines and utilizing maximum sidelobe decay (MSD) windows. Distinct from the existing approaches, the proposed approach makes use of the DFT peak as well as the DTFT spectral lines at any positions inside the main lobe of the weighted signal spectrum. The mean squared error (MSE) formula is derived. As shown by the simulation results, the presented approach is more approximate to the Cramer-Rao lower bound (CRLB) than the competing approaches with no increase in computational load.

Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets. In addition, we develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes. Specifically, we propose a learnable color space converter (CSC) to better facilitate rain removal in the Y channel, as nighttime rain is more pronounced in the Y channel compared to the RGB color space. To capture illumination information for guiding nighttime deraining, implicit illumination guidance is introduced enabling the learned features to improve the model's robustness in complex scenarios. Extensive experiments show the value of our dataset and the effectiveness of our method. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net.

This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions, by learning representative visual cues for complex aggregations of motion types. A total of 68 participants registered for the competition, and 9 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.