Thermal infrared image coloring method and evaluation method based on edge consistency

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.

Automated summary

Thermal imaging cameras have broad application prospects. However, coloring thermal infrared grayscale images is more difficult. This study proposes an unsupervised learning scheme based on CycleGAN, which improves the coloring effect of thermal infrared grayscale images by increasing self-supervision and introducing a new edge similarity indicator.