NDLI: Denoising framework based on external prior guided rotational clustering

Content Provider	IET Digital Library
Author	Yan, Hang Yan, Zhan Chen, Jian Xuan, Duo
Abstract	The image denoising model based on non-local self-similarity prior (NSS) has received extensive attention in recent years because of the repeated structure of natural image patches. Similar patches collected by exploiting NSS prior are sparse, which can be used to estimate potential lowrank subspace. Meanwhile, the modelling of natural images, such as Gaussian mixture models (GMMs), has been successful in all aspects of computer vision by reducing the patterns of image patches. However, the version of its geometric transformation (e.g. rotational transformation) cannot be matched directly by using distance. How to further reduce the patterns of the patches by geometric prior and accelerate rotational matching through parallel calculation is an issue that needs to be solved. In this study, an external guided rotational matching denoising framework is proposed. The proposed framework combines non-local, sparse and low-rank image priors and we design a parallel computing scheme. They demonstrate the performance improvement of the proposed algorithm on images with strong rotational properties and the comparison with traditional state-of-the-art denoising methods. The scalability and effectiveness of the new framework are verified by simulation experiments in public and real datasets.
Starting Page	1777
Ending Page	1786
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0918
Journal	IET Image Processing
Publisher Date	2020-03-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Computer Vision Computer Vision And Image Processing Technique Data Handling Technique Estimation Theory External Guided Rotational Matching Denoising External Prior Guided Rotational Clustering Gaussian Mixture Model Gaussian Processes Geometric Transformation Geometry GMM Image Denoising Image Matching Image Recognition Integral Transforms Mixture Model Natural Image Patches Nonlocal Self-similarity Prior NSS Pattern Clustering Statistics Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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