Overview of Image Denoising Based on Deep Learning

被引:10
|
作者
Liu, Baozhong [1 ]
Liu, Jianbin [1 ]
机构
[1] Beijing Informat Sci & Technol Univ, Sch Comp, Beijing, Peoples R China
关键词
D O I
10.1088/1742-6596/1176/2/022010
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the advent of the artificial intelligence era, deep learning technology is widely used in various fields, showing a good advantage in image noise reduction. In order to let more scholars understand the progress of machine learning in the field of image noise reduction, the research on machine learning in image denoising is reviewed. This paper mainly introduces three kinds of models, such as convolutional neural network, pulse coupled neural network and wavelet neural network, which are commonly used in image denoising. The nonlocal mean noise reduction method based on machine learning is described as a concrete case. The purpose of the article is to clearly understand the latest developments in deep learning in the field of image noise reduction.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning
    Hu, Yong
    Xu, Shaoping
    Cheng, Xiaohui
    Zhou, Changfei
    Hu, Yufeng
    [J]. APPLIED SCIENCES-BASEL, 2023, 13 (09):
  • [22] Deep learning optical image denoising research based on principal component estimation
    Lu, Qianbo
    Liu, Chengxiu
    Feng, Wenlu
    Xiao, Qingxiong
    Wang, Xiaoxu
    [J]. APPLIED OPTICS, 2022, 61 (15) : 4412 - 4420
  • [23] Dynamic PET Image Denoising With Deep Learning-Based Joint Filtering
    He, Yuru
    Cao, Shuangliang
    Zhang, Hongyan
    Sun, Hao
    Wang, Fanghu
    Zhu, Huobiao
    Lv, Wenbing
    Lu, Lijun
    [J]. IEEE ACCESS, 2021, 9 : 41998 - 42012
  • [24] Bayer image demosaicking and denoising based on specialized networks using deep learning
    Khadidos, Alaa O.
    Khadidos, Adil O.
    Khan, Fazal Qudus
    Tsaramirsis, Georgios
    Ahmad, Awais
    [J]. MULTIMEDIA SYSTEMS, 2021, 27 (04) : 807 - 819
  • [25] Bayer image demosaicking and denoising based on specialized networks using deep learning
    Alaa O. Khadidos
    Adil O. Khadidos
    Fazal Qudus Khan
    Georgios Tsaramirsis
    Awais Ahmad
    [J]. Multimedia Systems, 2021, 27 : 807 - 819
  • [26] Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
    Kaur, Roopdeep
    Karmakar, Gour
    Imran, Muhammad
    [J]. APPLIED SCIENCES-BASEL, 2023, 13 (20):
  • [27] Deep Convolutional Dictionary Learning Denoising Method Based on Distributed Image Patches
    Yin, Luqiao
    Gao, Wenqing
    Liu, Jingjing
    [J]. ELECTRONICS, 2024, 13 (07)
  • [28] A Deep Learning Denoising Framework Based on FFDNet for SAR Image Change Detection
    Duan, Hongke
    Dong, Xiaorui
    You, Shuailin
    Han, Shijing
    [J]. PROCEEDINGS OF 2021 IEEE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2021), 2021, : 52 - 55
  • [29] ResDNN: deep residual learning for natural image denoising
    Singh, Gurprem
    Mittal, Ajay
    Aggarwal, Naveen
    [J]. IET IMAGE PROCESSING, 2020, 14 (11) : 2425 - 2434
  • [30] PET image denoising using unsupervised deep learning
    Cui, Jianan
    Gong, Kuang
    Guo, Ning
    Wu, Chenxi
    Meng, Xiaxia
    Kim, Kyungsang
    Zheng, Kun
    Wu, Zhifang
    Fu, Liping
    Xu, Baixuan
    Zhu, Zhaohui
    Tian, Jiahe
    Liu, Huafeng
    Li, Quanzheng
    [J]. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2019, 46 (13) : 2780 - 2789