HAIMNet: A Hierarchical Adaptive Interaction Modulation Network for Low-Light Image Enhancement

Abstract

Low-light image enhancement (LLIE) is essential for enabling reliable nighttime visual perception and improving the performance of downstream vision tasks, including object detection and image segmentation. Under complex illumination conditions, low-light images often suffer from insufficient luminance, loss of structural details, and unstable color reproduction. Existing methods struggle to simultaneously restore luminance, texture, and color in a coherent manner. This paper proposes a Hierarchical Adaptive Interaction Modulation Network (HAIMNet) designed for LLIE. The proposed method decouples luminance and chromaticity in the Horizontal/Vertical-Intensity (HVI) color space, and enhances luminance-texture consistency through an inter-branch attention-modulation block (IAMB). Furthermore, a cross-branch gated affine fusion module (CGAF) is introduced to calibrate features between luminance and chromatic-structural representations, reduce color deviations, and enhance perceptual consistency. Extensive experiments on 11 public datasets demonstrate the effectiveness, robustness, and generalization capability of HAIMNet. The enhanced results exhibit high naturalness and stability under extremely dark and complex illumination conditions. Our code is available at: https://github.com/ZekeWang13/HAIMNet