# Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization

arXiv: https://arxiv.org/abs/2601.01103 · HTML: https://mlsanity.com/paper/2601.01103/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Learning A Sparse Transformer Network for Effective Image Deraining](https://mlsanity.com/paper/2303.11950/) (2023)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [PCSGAN: Perceptual Cyclic-Synthesized Generative Adversarial Networks for Thermal and NIR to Visible Image Transformation](https://mlsanity.com/paper/2002.07082/) (2020)
- [Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation](https://mlsanity.com/paper/2308.03348/) (2023)
- [Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues](https://mlsanity.com/paper/2107.09237/) (2021)
- [Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation](https://mlsanity.com/paper/2404.16685/) (2024)
- [Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation](https://mlsanity.com/paper/2312.16040/) (2023)
- [ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba](https://mlsanity.com/paper/2408.08087/) (2024)
- [Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging](https://mlsanity.com/paper/2503.01193/) (2025)

## Abstract (arXiv)

We present HAQAGen, a unified generative model for resolution-invariant NIR-to-RGB colorization that balances chromatic realism with structural fidelity. The proposed model introduces (i) a combined loss term aligning the global color statistics through differentiable histogram matching, perceptual image quality measure, and feature based similarity to preserve texture information, (ii) local hue-saturation priors injected via Spatially Adaptive Denormalization (SPADE) to stabilize chromatic reconstruction, and (iii) texture-aware supervision within a Mamba backbone to preserve fine details. We introduce an adaptive-resolution inference engine that further enables high-resolution translation without sacrificing quality. Our proposed NIR-to-RGB translation model simultaneously enforces global color statistics and local chromatic consistency, while scaling to native resolutions without compromising texture fidelity or generalization. Extensive evaluations on FANVID, OMSIV, VCIP2020, and RGB2NIR using different evaluation metrics demonstrate consistent improvements over state-of-the-art baseline methods. HAQAGen produces images with sharper textures, natural colors, attaining significant gains as per perceptual metrics. These results position HAQAGen as a scalable and effective solution for NIR-to-RGB translation across diverse imaging scenarios. Project Page: https://rajeev-dw9.github.io/HAQAGen/