Super-resolution reconstruction of variable length infrared image sequences based on convolutional neural networks and pixel shuffling

Infrared image super-resolution reconstruction technology can improve image resolution without changing the hardware of the imaging system, and has high cost-effectiveness. In this paper, a super-resolution reconstruction method based on convolutional neural network and pixel shuffle is proposed for the variable length infrared image sequences. Global residual learning and local residual block are introduced to accelerate the convergence speed of the network. Non-local residual block, progressive fusion residual blocks and pixel shuffle module are used to learn the long-distance time information and rich spatial information of infrared low-resolution image sequences. In addition to the fidelity evaluation indexes commonly used in current representative super-resolution reconstruction methods, we also introduce visual perception and image sharpness evaluation functions for perceptual evaluation. The network in this paper is trained and tested on real-world multi-frame infrared images. The experimental results show that the proposed method has advantages in obtaining better perception quality.