Abstract
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-/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 2.55dB in the NIR-to-RGB spectral domain translation task in terms of PSNR.
Original language | English |
---|---|
Title of host publication | 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP) |
Publisher | IEEE |
Number of pages | 5 |
ISBN (Electronic) | 9798350359855 |
ISBN (Print) | 9798350359862 |
DOIs | |
Publication status | Published - Dec 2023 |
Event | 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023 - Jeju, Korea, Republic of Duration: 4 Dec 2023 → 7 Dec 2023 https://ieeexplore.ieee.org/xpl/conhome/10402600/proceeding |
Publication series
Name | IEEE Visual Communications and Image Processing (VCIP) |
---|---|
Publisher | IEEE |
ISSN (Print) | 1018-8770 |
ISSN (Electronic) | 2642-9357 |
Conference
Conference | 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023 |
---|---|
Country/Territory | Korea, Republic of |
City | Jeju |
Period | 4/12/23 → 7/12/23 |
Internet address |
User-Defined Keywords
- Near-Infrared image colorization
- domain adaptation
- Generative Adversarial Network
- attention mechanism