Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections

Abstract

Image super-resolution technology successfully overcomes the limitation of excessively large pixel size in infrared detectors and meets the increasing demand for high-resolution infrared image information. In this paper, the super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections is reported. The introduction of global residual learning and local residual learning reduces computational complexity and accelerates network convergence. Multiple convolution layers and deconvolution layers respectively implement the extraction and restoration of the features in infrared images. Skip connections and channel fusion are introduced to the network to increase the number of feature maps and promote the deconvolution layers to restore image details. Compared with the other previously proposed methods for infrared information restoration, our proposed method shows obvious advantages in the ability of high-resolution details acquisition.