TY - JOUR
T1 - Thermal infrared image coloring method and evaluation method based on edge consistency
AU - Cai, Jiaming
AU - Tang, Xin
AU - Hu, Yao
AU - Zhang, Shaohui
AU - Hao, Qun
N1 - Publisher Copyright:
© 2023
PY - 2023/12
Y1 - 2023/12
N2 - Thermal imaging cameras have broad application prospects due to the characteristics of their shooting principles. With the development of deep learning technology, thermal infrared grayscale images can be transformed into RGB color images through a grayscale image colorization model, which provides more application possibilities for thermal infrared grayscale images. However, thermal infrared grayscale images are more difficult to color than visible and near-infrared grayscale images due to their lack of pixel-level matching visible light color map data. Therefore, we propose an unsupervised learning scheme based on CycleGAN, which reduces the loss of effective edge information and suppresses the generation of abnormal edge information during the colorization process by increasing the self-supervision of edge information in the cycle process. In addition, we propose a new edge similarity indicator Edge Consistency Index Measure (ECIM), which evaluates the quality of the coloring results from the perspective of edge consistency before and after colorization. By comparing with the existing methods, we show that our method is better than the existing methods in coloring effect, and the proposed ECIM can well describe the consistency of the edges before and after colorization.
AB - Thermal imaging cameras have broad application prospects due to the characteristics of their shooting principles. With the development of deep learning technology, thermal infrared grayscale images can be transformed into RGB color images through a grayscale image colorization model, which provides more application possibilities for thermal infrared grayscale images. However, thermal infrared grayscale images are more difficult to color than visible and near-infrared grayscale images due to their lack of pixel-level matching visible light color map data. Therefore, we propose an unsupervised learning scheme based on CycleGAN, which reduces the loss of effective edge information and suppresses the generation of abnormal edge information during the colorization process by increasing the self-supervision of edge information in the cycle process. In addition, we propose a new edge similarity indicator Edge Consistency Index Measure (ECIM), which evaluates the quality of the coloring results from the perspective of edge consistency before and after colorization. By comparing with the existing methods, we show that our method is better than the existing methods in coloring effect, and the proposed ECIM can well describe the consistency of the edges before and after colorization.
KW - Deep learning
KW - Edge consistency index measure
KW - Edge consistency loss
KW - Style transfer
KW - Thermal to visible
KW - Unsupervised
UR - http://www.scopus.com/inward/record.url?scp=85174036591&partnerID=8YFLogxK
U2 - 10.1016/j.infrared.2023.104946
DO - 10.1016/j.infrared.2023.104946
M3 - Article
AN - SCOPUS:85174036591
SN - 1350-4495
VL - 135
JO - Infrared Physics and Technology
JF - Infrared Physics and Technology
M1 - 104946
ER -