NDLI: Image patch prior learning based on random neighbourhood resampling for image denoising

Content Provider	IET Digital Library
Author	Ji, Jian Wei, Jiajie Fan, Guoliang Bai, Mengqi Huang, Jingjing Miao, Qiguang
Abstract	Image patch priors become a popular tool for image denoising. The Gaussian mixture model (GMM) is remarkably effective in modelling natural image patches. However, GMM prior learning using the expectation maximisation (EM) algorithm is sensitive to the initialisation, often leading to low convergence rate of parameter estimation. In this study, a novel sampling method called random neighbourhood resampling (RNR) is proposed to improve the accuracy and efficiency of parameter estimation. An enhanced GMM (EGMM) learning algorithm is further developed by incorporating RNR into the EM algorithm to initialise and update the GMM prior. The learned EGMM prior is applied in the expected patch log-likelihood (EPLL) framework for image denoising. The effectiveness and performance of the proposed RNR and EGMM algorithm are demonstrated via extensive experimental results comparing with the state-of-the-art image denoising methods, the experimental results show the higher PSNR result of the denoised images using the proposed method. Meanwhile, the authors verified that the proposed method can efficiently reduce the time of image denoising compared with the basic EPLL method.
Starting Page	838
Ending Page	844
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5403
Journal	IET Image Processing
Publisher Date	2019-12-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique EGMM Algorithm EM Algorithm Enhanced GMM Learning Algorithm EPLL Framework Expectation Maximisation Algorithm Expectation-maximisation Algorithm Expected Patch Log-likelihood Framework Gaussian Mixture Model Gaussian Processes Image Denoising Image Denoising Method Image Patch Prior Learning Knowledge Engineering Technique Learning in AI Mixture Model Natural Image Patches Optical, Image And Video Signal Processing Parameter Estimation Random Neighbourhood Resampling RNR Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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