Denoising framework based on external prior guided rotational clustering. Issue 9 (29th May 2020)

• Record Type: Journal Article
• Authors: Yan, Hang; Yan, Zhan; Chen, Jian; Xuan, Duo
• Is Part Of: IET image processing. Volume 14:Issue 9(2020)
• Page Start: 1777
• Page End: 1786
• Publication Date: 2020-05-29
• Subjects: image denoising -- computer vision -- Gaussian processes -- mixture models -- estimation theory -- transforms -- image matching -- pattern clustering -- geometry; external prior guided rotational clustering -- image denoising -- nonlocal self‐similarity prior -- NSS -- natural image patches -- Gaussian mixture models -- computer vision -- geometric transformation -- external guided rotational matching denoising -- GMM; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0918
• Languages: English
• ISSNs: 1751-9659

Abstract

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.