Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images

Abstract

The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution reconstruction of infrared images that preserves the edge structure and better visual quality is still challenging. Aiming at the problems of low resolution and unclear edges of infrared images, this work proposes a two-stage generative adversarial network model to reconstruct realistic superresolution images from four times downsampled infrared images. In the first stage of the generative adversarial network, it focuses on recovering the overall contour information of the image to obtain clear image edges; the second stage of the generative adversarial network focuses on recovering the detailed feature information of the image and has a stronger ability to express details. The infrared image superresolution reconstruction method proposed in this work has highly realistic visual effects and good objective quality evaluation results.

Tunnel Lining Defect Identification Method Based on Small Sample Learning

“…Based on the idea of game theory, they constructed a network of mutually opposing generators and discriminators to generate images, and the image generation effect was relatively good. At present, GAN has been successfully applied in image repair and restoration, animation generation, superresolution image reconstruction, and other fields [4]. Arjovsky et al [5] proposed to use Wasserstein distance to replace KL divergence and JS divergence in the original GAN objective function to construct a WGAN network in view of problems such as training instability and gradient disappearance in the original GAN.…”