Abstract：Infrared image super-resolution is a field of image super-resolution reconstruction，and methods via deep learning tend to focus on RGB images with rich colors and textures，which are inefficient for extracting features from infrared images with uniform pixel distribution，low contrast，and loss of high-frequency details.This paper proposes a super-resolution method based on generative adversarial networks（GAN）that can reconstruct detailed textures for infrared images.Its generator is constructed by a series of Lightweight attention residual blocks（LARB），which can extract pixel feature information at low cost and high efficiency from infrared images.It combines with perception loss before feature activation，Huber loss and Wasserstein distance to converge stably and reduce artifacts after image reconstruction.We use near infrared image datasets and linear grayscale transform of infrared feature maps to help our model learn more texture features.The experimental results show that in the PSNR comparison，our model generator with a parameter of 542K is significantly ahead of SRGAN with a parameter of 1518K，and is close to ESRGAN with a parameter of 16697K.In the SSIM comparison，some test results are higher than ESRGAN，which indicating the effectiveness of our method.
2022, Vol. 43 
