Abstract
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.
Abstract (translated)
NIR-to-RGB spectral domain translation是一个具有挑战性的任务，因为NIR输入和RGB输出的固有光谱映射歧义。因此，现有的方法无法在保持纹理细节保真度和实现多样色彩变化之间实现和谐。在本文中，我们提出了一种多尺度HSV颜色特征嵌入网络（MCFNet），将映射过程分解为包括NIR纹理维护、粗几何重建和RGB颜色预测三个子任务的三个子任务。因此，我们提出了每个相应子任务的关键模块：纹理保留模块（TPB）、HSV颜色特征嵌入模块（HSV-CFEM）和几何重建模块（GRM）。这些模块通过一系列逐渐升高的分辨率，以尺度和谐的方式贡献于我们的MCFNet方法，通过一系列自适应纹理映射，实现对NIR图像颜色化的巨大性能提升。所提出的MCFNet在NIR图像颜色化任务中取得了显著的性能提升。代码发布在：https://这个URL。
URL
https://arxiv.org/abs/2404.16685
