Detail-Preserving Image Denoising via Adaptive Clustering and Progressive PCA Thresholding

Detail-Preserving Image Denoising via Adaptive Clustering and Progressive PCA Thresholding

This paper proposes a detail-preserving image denoising method via cluster-wise progressive principal component analysis (PCA) thresholding based on the Marchenko-Pastur (MP) law in random matrix theory. According to random matrix theory, an efficient and stable noise-level estimation method is also...

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Bibliographic Details
Main Authors: Wenzhao Zhao, Yisong Lv, Qiegen Liu, Binjie Qin
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Detail-preserving denoising
random matrix theory
adaptive clustering
image decomposition
low-rank approximation (LRA)
principal component analysis
Online Access:https://ieeexplore.ieee.org/document/8169017/

Internet

https://ieeexplore.ieee.org/document/8169017/

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