Super-resolution of images based on Generative Adversarial Network and noise distribution

Abstract:

Existing image super-resolution reconstruction methods take less into account the noise information contained in real low-resolution images, which will affect the quality of image reconstruction. Inspired by the real image denoising algorithm, this paper introduces a noise distribution collection network to collect noise distribution information of low-resolution images, and adopts a model design of Generative Adversarial Network to improve the reconstruction quality of noisy images. The noise distribution information will be input to the super-resolution reconstruction network and the discriminant network respectively. During the reconstruction process, the noise is removed during while ensuring the recovery of useful high-frequency information, because the ability of the discriminant network has an important impact on the performance of the entire model, the U-Net network is selected to obtain better gradient information feedback. Comparison with the classical image super-resolution reconstruction methods and ablation experiments,the resluts show that the proposed model obtains better performance in the noisy low-resolution image reconstruction task after using the noise collection network and the U-Net discriminant network.