Low-light image enhancement network based on multiscale dynamic frequency domain optimization

Abstract

Existing low-light enhancement methods often suffer from blur, noise, and inconsistent brightness, largely because they overlook frequency-domain characteristics. To address this, we propose the Multi-scale Dynamic Frequency-domain Optimization Network (MDFNet). Specifically, we introduce a Multi-Domain Attention Module (MDAM) to integrate spatial features with frequency-domain global context, and a Dynamic Frequency-domain Fusion Module (DFFM) that employs dynamic filters for detail refinement, noise suppression, and local brightness correction. Furthermore, a learnable fusion mechanism balances high- and low-frequency signals to ensure uniform illumination. Comprehensive experiments demonstrate that MDFNet outperforms state-of-the-art methods in terms of both objective metrics and visual quality.