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NIR-to-RGB spectral domain translation is a challenging task due to themapping ambiguities, and existing methods show limited learning capacities. Toaddress these challenges, we propose to colorize NIR images via a multi-scaleprogressive feature embedding network (MPFNet), with the guidance of grayscaleimage colorization. Specifically, we first introduce a domain translationmodule that translates NIR source images into the grayscale target domain. Byincorporating a progressive training strategy, the statistical and semanticknowledge from both task domains are efficiently aligned with a series ofpixel- and feature-level consistency constraints. Besides, a multi-scaleprogressive feature embedding network is designed to improve learningcapabilities. Experiments show that our MPFNet outperforms state-of-the-artcounterparts by 2.55 dB in the NIR-to-RGB spectral domain translation task interms of PSNR.

Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation