  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.
