Image Denoising Method based on Deep Learning using Improved U-net

Abstract

Various methods, including block-matching and 3D filtering (BM3D), have been proposed for image denoising. Recently, studies on deep learning methods for image denoising have been on the rise. In this paper, we propose a new structure for a deep neural network that improves image denoising performance. Among the existing deep neural networks, we improve U-net, which is widely used for image restoration, through the inclusion of pre-processing and post-processing and by modifying each of its stages. Extensive simulations show that the proposed structure performs very well for a wide range of noise levels with a single trained parameter, and it exhibits superior image denoising performance compared to conventional deep neural networks.