# Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation

arXiv: https://arxiv.org/abs/2404.16685 · HTML: https://mlsanity.com/paper/2404.16685/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues](https://mlsanity.com/paper/2312.11119/) (2023)
- [Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation](https://mlsanity.com/paper/2308.03348/) (2023)

## Built on by (most cited first, 3 of 3)

- [ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba](https://mlsanity.com/paper/2408.08087/) (2024)
- [Learning Physics-Informed Color-Aware Transforms for Low-Light Image Enhancement](https://mlsanity.com/paper/2504.11896/) (2025)
- [Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization](https://mlsanity.com/paper/2601.01103/) (2026)

## Abstract (arXiv)

The NIR-to-RGB spectral domain translation is a formidable task due to the inherent spectral mapping ambiguities within NIR inputs and RGB outputs. Thus, existing methods fail to reconcile the tension between maintaining texture detail fidelity and achieving diverse color variations. In this paper, we propose a Multi-scale HSV Color Feature Embedding Network (MCFNet) that decomposes the mapping process into three sub-tasks, including NIR texture maintenance, coarse geometry reconstruction, and RGB color prediction. Thus, we propose three key modules for each corresponding sub-task: the Texture Preserving Block (TPB), the HSV Color Feature Embedding Module (HSV-CFEM), and the Geometry Reconstruction Module (GRM). These modules contribute to our MCFNet methodically tackling spectral translation through a series of escalating resolutions, progressively enriching images with color and texture fidelity in a scale-coherent fashion. The proposed MCFNet demonstrates substantial performance gains over the NIR image colorization task. Code is released at: https://github.com/AlexYangxx/MCFNet.