Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement

• URL: http://arxiv.org/abs/2510.09450v1
• Date: Fri, 10 Oct 2025 15:00:31 GMT
• Title: Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement
• Authors: Ruirui Lin, Guoxi Huang, Nantheera Anantrasirichai,
• Abstract summary: Low-light video enhancement is challenging due to noise, low contrast, and color degradations.<n>We present DWTA-Net, a novel framework that exploits short- and long-term temporal cues.<n>We show that DWTA-Net effectively suppresses noise and artifacts, delivering superior visual quality compared with state-of-the-art methods.
• Score: 6.8703489542630445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradations. Learning-based approaches offer fast inference but still struggle with heavy noise in real low-light scenes, primarily due to limitations in effectively leveraging temporal information. In this paper, we address this issue with DWTA-Net, a novel two-stage framework that jointly exploits short- and long-term temporal cues. Stage I employs Visual State-Space blocks for multi-frame alignment, recovering brightness, color, and structure with local consistency. Stage II introduces a recurrent refinement module with dynamic weight-based temporal aggregation guided by optical flow, adaptively balancing static and dynamic regions. A texture-adaptive loss further preserves fine details while promoting smoothness in flat areas. Experiments on real-world low-light videos show that DWTA-Net effectively suppresses noise and artifacts, delivering superior visual quality compared with state-of-the-art methods.

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