Deep learning-based image super-resolution restoration for mobile infrared imaging system

Mobile infrared imaging system (MIIS) has important applications in the areas of the military affairs, medical diagnosis, and industrial detection. However, MIIS usually suffers the problems of the low resolution, low contrast, and noise interference. To solve these problems, we propose a deep learning-based image superresolution restoration method for the MIIS. We design a multiscale feature distillation residual network to retain image features at various stages during the training process. The network uses dilated convolution to expand the receptive field and outputs the super-resolution (SR) infrared image with a sub-pixel method. Many infrared images that are captured by a MIIS are used for experiments. Experimental results indicate that the proposed method can realize the infrared image SR restoration and performs outstandingly against the four recently published SR methods both in visual quality and indicator performance. The proposed method is helpful for the practical applications of the MIIS.