Low-Light Video Enhancement via Spatial-Temporal Consistent Illumination and Reflection Decomposition

• URL: http://arxiv.org/abs/2405.15660v1
• Date: Fri, 24 May 2024 15:56:40 GMT
• Title: Low-Light Video Enhancement via Spatial-Temporal Consistent Illumination and Reflection Decomposition
• Authors: Xiaogang Xu, Kun Zhou, Tao Hu, Ruixing Wang, Hujun Bao,
• Abstract summary: Low-Light Video Enhancement (LLVE) seeks to restore dynamic and static scenes plagued by severe invisibility and noise. One critical aspect is formulating a consistency constraint specifically for temporal-spatial illumination and appearance enhanced versions. We present an innovative video Retinex-based decomposition strategy that operates without the need for explicit supervision.
• Score: 68.6707284662443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-Light Video Enhancement (LLVE) seeks to restore dynamic and static scenes plagued by severe invisibility and noise. One critical aspect is formulating a consistency constraint specifically for temporal-spatial illumination and appearance enhanced versions, a dimension overlooked in existing methods. In this paper, we present an innovative video Retinex-based decomposition strategy that operates without the need for explicit supervision to delineate illumination and reflectance components. We leverage dynamic cross-frame correspondences for intrinsic appearance and enforce a scene-level continuity constraint on the illumination field to yield satisfactory consistent decomposition results. To further ensure consistent decomposition, we introduce a dual-structure enhancement network featuring a novel cross-frame interaction mechanism. This mechanism can seamlessly integrate with encoder-decoder single-frame networks, incurring minimal additional parameter costs. By supervising different frames simultaneously, this network encourages them to exhibit matching decomposition features, thus achieving the desired temporal propagation. Extensive experiments are conducted on widely recognized LLVE benchmarks, covering diverse scenarios. Our framework consistently outperforms existing methods, establishing a new state-of-the-art (SOTA) performance.

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