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.

通过引入创新的基于 Retinex 的视频分解策略，结合动态相邻帧的相关性和场景级连续性约束，以及双结构增强网络的交互机制，该研究实现了在低光环境下视频的增强和一致分解，超越了现有的方法，达到了最新的性能水平。