Overview of Image Denoising Methods

Abstract

In real scenes, due to the imperfections of equipment and systems or the existence of low-light environments, the collected images are noisy. The images will also be affected by additional noise during the compression and transmission process, which will interfere with subsequent image segmentation and feature extraction processes. Traditional denoising methods use the non-local self-similarity (NLSS) characteristics of the image and the sparse representation in the transform domain, and the method based on block-matching and three-dimensional filtering (BM3D) shows a powerful image denoising performance. With the development of artificial intelligence, image denoising methods based on deep learning have achieved outstanding performance. But so far, there is almost no relevant research on the comprehensive comparison of image denoising methods. Aiming at the traditional image denoising methods and the image denoising methods based on deep neural networks that have emerged in recent years, this paper first introduces the basic framework of the classic traditional denoising and deep neural network denoising methods and classifies and summarizes the denoising methods. Then the existing denoising methods are analyzed and compared quantitatively and qualitatively on the public denoising data set. Finally, this paper points out some potential challenges and future research directions in the field of image denoising.

Keywords

• non-local similarity
• transform domain
• block matching technology
• deep neural network