IDM-Net: A Lightweight Illumination-Decoupled Modulation Network for Low-Light Image Enhancement

2026-09-25

AI Summary

This study addresses the challenge faced by lightweight low-light enhancement models in simultaneously achieving brightness restoration and color fidelity, proposing a novel framework termed IDM-Net. To our knowledge, this work is the first to introduce a decoupled illumination channel as an explicit guiding prior within a dual-encoder architecture. Specifically, an Illumination-Guided Modulation (IGM) module injects multi-scale illumination information through a spatially adaptive affine mechanism, while Feature Refinement Blocks (FRBs) effectively suppress artifacts. Experimental results demonstrate that IDM-Net achieves highly competitive performance across multiple benchmarks. By overcoming the limitations of conventional approaches that solely optimize brightness, the proposed method realizes an excellent balance between enhancement quality and computational efficiency.

Abstract

Low-light image enhancement (LLIE) remains challenging for lightweight models because illumination restoration and color fidelity are difficult to optimize simultaneously in the RGB color space. Although recent color-decoupled methods separate luminance and chrominance representations, they primarily optimize luminance as an enhancement target, leaving its potential as an explicit guidance prior largely unexplored during feature reconstruction. To address this limitation, we propose IDM-Net, a lightweight Illumination-Decoupled Modulation Network for low-light image enhancement. IDM-Net adopts a dual-encoder architecture consisting of a structure encoder that extracts multi-scale appearance features from the RGB image and a lightweight illumination encoder that learns illumination priors from the decoupled luminance (Y) channel. To effectively exploit these priors, we introduce an Illumination-Guided Modulation (IGM) module that injects multi-scale illumination cues into the decoder through spatially adaptive affine modulation, enabling accurate brightness restoration while preserving natural color consistency. Furthermore, we design a lightweight Feature Refinement Block (FRB) to progressively suppress degradation artifacts and recover fine-grained image details during reconstruction. Extensive experiments on multiple standard low-light image enhancement benchmarks demonstrate that IDM-Net achieves competitive performance among lightweight LLIE methods while maintaining an excellent balance between restoration quality and computational efficiency.

Problem

• Low-light image enhancement
• Lightweight model
• Illumination restoration
• Color fidelity
• Illumination prior

Innovation

• Low-Light Image Enhancement
• Illumination-Decoupled Modulation
• Dual-Encoder Architecture
• Spatially Adaptive Affine Modulation
• Lightweight Network