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.

翻译：近红外到RGB光谱域转换是一项艰巨的任务，因为近红外输入与RGB输出之间存在固有的光谱映射歧义。因此，现有方法难以在保持纹理细节保真度与实现多样化颜色变化之间取得平衡。本文提出了一种多尺度HSV颜色特征嵌入网络（MCFNet），将映射过程分解为三个子任务：近红外纹理保持、粗略几何重建和RGB颜色预测。针对每个子任务，我们提出了三个关键模块：纹理保持模块（TPB）、HSV颜色特征嵌入模块（HSV-CFEM）和几何重建模块（GRM）。这些模块使得MCFNet能够通过一系列递增的分辨率逐步处理光谱转换，以尺度一致的方式逐步丰富图像的色彩与纹理保真度。所提出的MCFNet在近红外图像着色任务上展现了显著的性能提升。代码已开源：https://github.com/AlexYangxx/MCFNet。