🤖 AI Summary
This work addresses the challenges of low-light image enhancement, which is prone to noise amplification, artifacts, and color distortion, and often relies solely on RGB data, limiting its ability to model illumination and scene structure effectively. To overcome these limitations, the authors propose M2Retinexformer, a novel framework that integrates depth, luminance priors, and semantic features into the Retinex model for the first time. It introduces a multi-scale cross-attention fusion mechanism and an adaptive gating strategy guided by the reliability of auxiliary cues, dynamically balancing self-attention and cross-modal attention to enable progressive enhancement. Extensive experiments demonstrate that M2Retinexformer significantly outperforms Retinexformer and other state-of-the-art methods across multiple benchmarks—including LOL, SID, SMID, and SDSD—achieving superior performance in brightness restoration, color fidelity, and detail reconstruction.
📝 Abstract
Low-light image enhancement is challenging due to complex degradations, including amplified noise, artifacts, and color distortion. While Retinex-based deep learning methods have achieved promising results, they primarily rely on single-modality RGB information. We propose M2Retinexformer (Multi-Modal Retinexformer), a novel framework that extends Retinexformer by incorporating depth cues, luminance priors, and semantic features within a progressive refinement pipeline. Depth provides geometric context that is invariant to lighting variations, while luminance and semantic features offer explicit guidance on brightness distribution and scene understanding. Modalities are extracted at multiple scales and fused through cross-attention, with adaptive gating dynamically balancing illumination-guided self-attention and cross-attention based on the reliability of auxiliary cues. Evaluations on the LOL, SID, SMID, and SDSD benchmarks demonstrate overall improvements over Retinexformer and recent state-of-the-art methods. Code and pretrained weights are available at https://github.com/YoussefAboelwafa/M2Retinexformer