Self-Supervised Infrared Video Super-Resolution Based on Deformable Convolution

Infrared video often encounters low resolution, which makes it difficult to perform the target detection and recognition task. Super-resolution (SR) is an effective technology to enhance the resolution of infrared video. However, the existing SR method of infrared image is basically a single image SR, which restricts the performance of SR due to ignoring the strong inter-frame correlation in video. We propose a self-supervised SR method for infrared video that can estimate the blur kernel and generate paired data from raw low-resolution infrared video itself, without the need for additional high-resolution videos for supervision. Furthermore, to overcome the limitations of optical flow prediction in handling complex motion, a deformable convolutional network is introduced to adaptively learn motion information to capture more accurate, tiny motion changes between adjacent images in an infrared video. Experimental results show that the proposed method can achieve an outstanding performance of restored image in both visual effect and quantitative metrics.