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: this https URL

通过将过程分解为 NIR 纹理维护、粗糙几何重建和 RGB 颜色预测，我们提出了一种多尺度 HSV 颜色特征嵌入网络（MCFNet），通过一系列逐渐增加分辨率的方法系统地解决光谱转换问题，以在规模协调的方式下逐步丰富图像的颜色和纹理保真度。