EHENet: edge-guided hybrid enhancement network for low-light light field image enhancement

Abstract

Low-light light field (L3F) images suffer from severe structural degradation, including low contrast, blurred edges, and heavy noise, which disrupts angular consistency. Existing single-image enhancement methods fail to exploit the spatial-angular consistency of light field (LF) images, while L3F enhancement methods struggle in low-light scenarios with extremely low contrast, often resulting in over-smoothed edges and the loss of geometric details. To address these issues, we propose the Edge-guided Hybrid Enhancement Network (EHENet), an efficient network integrating structural priors and spatial-angular extraction. We propose a structural prior embedding strategy that employs Scharr operators and Gaussian filtering to explicitly model spatial edges. Furthermore, we design the Global Feature Enhancement (GFE) block to extract spatial-angular correlation. Experiments show EHENet significantly outperforms state-of-the-art methods in both quality and efficiency.