Imagine Denoising Methods Based on GANs

Abstract

Abstract. With the continuous development of technology, peoples demand for image clarity is also constantly increasing. Whether in the fields of medical imaging, aerospace, or people's daily lives, noise in images seriously affects their clarity. Therefore, how to remove noise on the premise of preserving image details has now become a hot research topic. In the domain of image denoising, both early spatial domain filtering methods and recently proposed convolutional neural network models have certain limitations. Compared to other denoising methods, GANs can better remove noise from images and improve image quality. Therefore, this article summarizes and organizes image denoising methods based on GAN models. This article explains four GAN based image denoising methods, namely GAN, WGAN, DNGAN, and GCBD, from the perspectives of framework structure, advantages and disadvantages, and application fields. At the same time, this article also analyzes the development trend of GAN application in image denoising and makes prospects.