Image Denoising Algorithm Based on Generative Adversarial Network. Issue 2 (June 2021)

• Record Type: Journal Article
• Authors: Liu, Gaoyuan; Zhong, Guangyuan; Zhao, Huiqi
• Is Part Of: Journal of physics. Volume 1952:Issue 2(2021)
• Publication Date: 2021-06
• Subjects: Image denoising -- GAN -- residual structure -- dense connection; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1952/2/022022
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Image denoising is an important research direction of image restoration. With the increasing requirements of image quality, image denoising has been widely concerned by scholars at home and abroad. This paper studies the problem of noise image denoising, using improved sparse representation algorithm and deep learning technology to denoise the noise image. In this paper, a de-noising model based on generative countermeasure network is proposed. The residual algorithm and dense connection are added to the network layer of learning image noise information features. At the same time, the discriminator is trained to judge whether the generated image is true or false, so as to avoid boundary blur while de-noising. Zero filling convolution is used to ensure the consistency of input and output characteristic dimensions of each layer. The output of network model is image noise information. Simulation results show that the algorithm has good denoising performance.