M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement
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 multi...
| Authors | Youssef Aboelwafa, Hicham G. Elmongui, Marwan Torki |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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