A Brief Review of Image Restoration Techniques Based on Generative Adversarial Models

Abstract

Images are possibly degraded by various reasons, the typical forms of degradation are: blur, noise, low resolution, and etc. Image restoration techniques try to recover the degraded images to the original images with maximum fidelity. Image restoration is a challenging task and also an import research area in image processing. During the decades, researchers have proposed many restoration methods such as inverse filter, Weiner filter, wavelet analysis, support vector machine, and etc. Recently, deep learning has been increasingly popular among researchers and has obtained remarkable results. In this paper, we briefly review the approaches based on generative adversarial networks (GANs) for image restoration. The typical GANs based restoration methods for image super-resolution, image denoising, image inpainting and image deblurring are introduced and discussed.