Open AccessResearch Progress in Image Denoising Algorithms Based on Deep Learning
Author(s) -
Ruikai Cai
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1345/4/042055
Subject(s) - computer science , artificial intelligence , field (mathematics) , image processing , noise reduction , digital image processing , deep learning , machine learning , algorithm , image (mathematics) , data science , mathematics , pure mathematics
Images constitute the main source of information to people. Digital images are vital means of obtaining, processing, analyzing, and sharing information in the era of information. Now they have been deeply incorporated into every aspect of people’s production and life, generating considerable social and economic benefits. Thus improving the quality of images and reducing the negative impact of image noises to subsequent image processing have been two important research topics. Image processing technology has been combined with such research fields as cognitive psychology, machine learning, machine vision, and deep learning in recent years, which lead to an unprecedented development level and breakthroughs. Therefore, the study of image denoising technology has profound theoretical significance and promising prospects in practical application. The paper mainly discusses the development course of applying deep learning technology to the image denoising field. Meanwhile, it introduces various classical denoising algorithms and focuses on the thoughts, advantages, and disadvantages of each algorithm. Furthermore, it discusses the challenges faced by deep learning in the denoising field and puts forward possible solutions.