Papers › ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba
ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba
Huiyu Zhai, Guang Jin, Xingxing Yang, Guosheng Kang
Translating NIR to the visible spectrum is challenging due to cross-domain complexities. Current models struggle to balance a broad receptive field with computational efficiency, limiting practical use. Although the Selective Structured State Space Model, especially the improved version, Mamba, excels in generative tasks by capturing long-range dependencies with linear complexity, its default approach of converting 2D images into 1D sequences neglects local context. In this work, we propose a simple but effective backbone, dubbed ColorMamba, which first introduces Mamba into spectral translation tasks. To explore global long-range dependencies and local context for efficient spectral translation, we introduce learnable padding tokens to enhance the distinction of image boundaries and prevent potential confusion within the sequence model. Furthermore, local convolutional enhancement and agent attention are designed to improve the vanilla Mamba. Moreover, we exploit the HSV color to provide multi-scale guidance in the reconstruction process for more accurate spectral translation. Extensive experiments show that our ColorMamba achieves a 1.02 improvement in terms of PSNR compared with the state-of-the-art method. Our code is available at https://github.com/AlexYangxx/ColorMamba.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Colorization | NIR2RGB VCIP Challange Dataset | ColorMamba | PSNR | 24.56 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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