Add model card and metadata
Browse filesThis PR adds a model card for M2Retinexformer, including:
- Metadata with the `image-to-image` pipeline tag.
- A link to the paper: [M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement](https://huggingface.co/papers/2605.12556).
- A link to the official GitHub repository.
- A summary of the model's architecture and the BibTeX citation.
README.md
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---
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pipeline_tag: image-to-image
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---
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# M2Retinexformer
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This repository contains the official weights for **M2Retinexformer** (Multi-Modal Retinexformer), introduced in the paper [M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement](https://huggingface.co/papers/2605.12556).
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- **Paper:** [M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement](https://huggingface.co/papers/2605.12556)
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- **Code:** [GitHub Repository](https://github.com/YoussefAboelwafa/M2Retinexformer)
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## Introduction
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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](https://arxiv.org/abs/2303.06705) by incorporating **depth cues**, **luminance priors**, and **semantic features** within a progressive refinement pipeline.
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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.
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## Citation
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If you find this work useful, please cite:
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```bibtex
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@misc{aboelwafa2026m2retinexformermultimodalretinexformerlowlight,
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title={M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement},
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author={Youssef Aboelwafa and Hicham G. Elmongui and Marwan Torki},
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year={2026},
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eprint={2605.12556},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2605.12556},
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}
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```
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## Acknowledgements
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This project is built on the baseline architecture of [Retinexformer](https://github.com/caiyuanhao1998/Retinexformer).
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