提出可适配任意分辨率的红外转彩色图像生成模型，兼顾色彩真实与细节清晰。
Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization
- 通过可微直方图匹配+感知质量损失+特征相似性，统一优化全局色彩与纹理。
- 引入空间自适应去归一化，稳定局部色度重建，提升色彩一致性。
- 基于Mamba架构实现细粒度纹理监督，适合高分辨率红外图像转彩应用。
我们提出HAQAGen，一种统一的生成模型，用于实现跨分辨率的红外到彩色图像转换，同时兼顾色彩真实感与结构保真度。该模型引入三项创新：(i) 通过可微直方图匹配、感知图像质量度量及基于特征的相似性构建联合损失，以保持全局色彩统计与纹理信息；(ii) 利用空间自适应去归一化（SPADE）注入局部色相-饱和度先验，稳定色彩重建；(iii) 在Mamba主干网络中加入纹理感知监督，保留精细细节。进一步设计自适应分辨率推理引擎，支持无损高分辨率转换。在FANVID、OMSIV、VCIP2020和RGB2NIR数据集上的广泛评估表明，相比现有最优方法，本模型在多种评价指标下均取得显著提升。生成图像具备更锐利纹理与自然色彩，感知评价指标显著改善。结果表明，HAQAGen是适用于多样成像场景的可扩展高效解决方案。
原文摘要 · Abstract (English)
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/
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