Research on WGAN-based Image Super-resolution Reconstruction Method

Nowadays, obtaining information from images is one of the main ways in which people obtain information. However, the images are affected by the acquisition of hardware equipment and transmission technology, which may result in the loss of certain data and the resolution to be reduced. How to recover low-resolution images into high-resolution images has grown to be a popular area of research for image processing. Deep learning techniques can discover more expressive features through adaptive learning from the dataset. However, there are problems with too many deep network parameters, blurred reconstructed images, and obvious human traces. To address these issues, this essay suggests the Deep Recursive Residual Subpixel Wasserstein Generative Adversarial Network (DRRSWGAN) using Deep Recursive Residual Subpixel Network (DRRSN) in the generative network to solve the problems of a large number of network parameters, relatively smooth reconstructed images and the presence of human traces. The Wasserstein Generative Adversarial Network (WGAN) is used in the discriminative network to improve the discriminative network of the GAN for the problem of training instability. Removing the Sigmod layer from the network, optimizing the loss function, and using the RMSProp algorithm for gradient optimization, allows the network to be more stable during training and better image reconstruction. The outcomes of the trial indicate this method improves both PSNR and SSIM metrics, and the image reconstruction results are better in terms of subjective perception. © (2023), (International Association of Engineers). All Rights Reserved.