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.

翻译：近红外到RGB光谱域翻译是一项具有挑战性的任务，原因在于映射歧义性，现有方法学习能力有限。针对这些挑战，我们提出通过多尺度渐进特征嵌入网络（MPFNet）对近红外图像进行着色，并以灰度图像着色作为引导。具体而言，我们首先引入一个域翻译模块，将近红外源图像转换为灰度目标域。通过结合渐进式训练策略，两个任务域中的统计和语义信息借助一系列像素级与特征级一致性约束得到高效对齐。此外，我们设计了一个多尺度渐进特征嵌入网络以提升学习能力。实验表明，在近红外到RGB光谱域翻译任务中，我们的MPFNet在峰值信噪比（PSNR）指标上相较于当前最优方法提升了2.55 dB。