Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition

TL;DR

This paper introduces a novel spatial-temporal decomposition approach for low-light video enhancement, utilizing cross-frame correspondence and a dual-structure network to achieve state-of-the-art results.

Contribution

It proposes a view-independent and view-dependent decomposition strategy combined with a dual-structure network for improved LLVE performance.

Findings

Outperforms existing LLVE methods on benchmark datasets

Achieves more consistent and visually pleasing enhancement results

Maintains low additional computational costs

Abstract

Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition strategy that incorporates view-independent and view-dependent components to enhance the performance of LLVE. We leverage dynamic cross-frame correspondences for the view-independent term (which primarily captures intrinsic appearance) and impose a scene-level continuity constraint on the view-dependent term (which mainly describes the shading condition) to achieve consistent and satisfactory decomposition results. To further ensure consistent decomposition, we introduce a dual-structure enhancement network featuring a cross-frame interaction mechanism. By supervising different frames simultaneously, this network encourages them to exhibit matching decomposition features. This mechanism can seamlessly integrate with encoder-decoder single-frame networks, incurring minimal additional parameter costs. Extensive experiments are conducted on widely recognized LLVE benchmarks, covering diverse scenarios. Our framework consistently outperforms existing methods, establishing a new SOTA performance.