Illumination-aware and structure-guided transformer for low-light image enhancement

Abstract

In this paper, we proposed a novel illumination-aware and structure-guided transformer that achieves efficient image enhancement by focusing on brightness degradation and precise high-frequency guidance. Specifically, low-light images often contain numerous regions with similar brightness levels but different spatial locations. However, existing attention mechanisms only compute self-attention using channel dimensions or fixed-size spatial blocks, which limits their ability to capture long-range features, making it challenging to achieve satisfactory image restoration quality. At the same time, the details of low-light images are mostly hidden in the darkness. However, existing models often give equal attention to both high-frequency and smooth regions, which makes it difficult to capture the details of deep degradation, resulting in blurry recovered image details. On the one hand, we introduced a dynamic brightness multi-domain self-attention mechanism that selectively focuses on spatial features within dynamic ranges and incorporates frequency domain information. This approach allows the model to capture both local details and global features, restoring global brightness while paying closer attention to regions with similar degradation. On the other hand, we proposed a global maximum gradient search strategy to guide the model’s attention towards high-frequency detail regions, thereby achieving a more fine-grained restored image. Extensive experiments on various benchmark datasets demonstrate that our method achieves state-of-the-art performance.