Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB
  Spectral Domain Translation
Xingxing Yang Jie Chen Zaifeng Yang
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.