This repository contains the official weights for
M2Retinexformer (Multi-Modal Retinexformer), introduced in the paper
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. M2Retinexformer is a novel framework that extends
Retinexformer by incorporating
depth cues,
luminance priors, and
semantic features within a progressive refinement pipeline.
Depth provides geometric context invariant to lighting variations, while luminance and semantic features offer explicit guidance on brightness distribution and scene understanding. These modalities are fused through cross-attention with adaptive gating to dynamically balance illumination-guided self-attention and cross-attention based on the reliability of auxiliary cues.
1@misc{aboelwafa2026m2retinexformermultimodalretinexformerlowlight,
2 title={M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement},
3 author={Youssef Aboelwafa and Hicham G. Elmongui and Marwan Torki},
4 year={2026},
5 eprint={2605.12556},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2605.12556},
9}
This project is built on the baseline architecture of
Retinexformer.
