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。

多尺度 HSV 颜色特征嵌入用于高保真近红外到 RGB 光谱转换

Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB  Spectrum Translation

NIR-to-RGB spectral domain translation is a challenging task due to the
mapping ambiguities, and existing methods show limited learning capacities. To
address these challenges, we propose to colorize NIR images via a multi-scale
progressive feature embedding network (MPFNet), with the guidance of grayscale
image colorization. Specifically, we first introduce a domain translation
module that translates NIR source images into the grayscale target domain. By
incorporating a progressive training strategy, the statistical and semantic
knowledge from both task domains are efficiently aligned with a series of
pixel- and feature-level consistency constraints. Besides, a multi-scale
progressive feature embedding network is designed to improve learning
capabilities. Experiments show that our MPFNet outperforms state-of-the-art
counterparts by 2.55 dB in the NIR-to-RGB spectral domain translation task in
terms of PSNR.

通过多尺度渐进特征嵌入网络（MPFNet）引导灰度图像上色的方法，解决了 NIR 到 RGB 光谱域翻译中的映射歧义和现有方法学习能力有限的问题，实验证明 MPFNet 在 NIR 到 RGB 光谱域翻译任务中相对于最先进的方法在 PSNR 方面提高了 2.55 dB。