Edge coherence-weighted second-order variational model for image denoising

High-order variational models have the ability to remove the staircase effect generated by the total variation regularizer. They, however, tend to blur object edges. To overcome this drawback, we introduce an edge coherence-weighted second-order (ECSO) model for image denoising. We propose novel reg...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2022-11, Vol.16 (8), p.2313-2320
Main Authors: Phan, Tran Dang Khoa, Tran, Thi Hoang Yen
Format: Article
Language:English
Subjects:
Algorithms
Coherence
Computer Imaging
Computer Science
Fast Fourier transformations
Image Processing and Computer Vision
Multimedia Information Systems
Noise reduction
Original Paper
Pattern Recognition and Graphics
Regularization
Signal,Image and Speech Processing
Vision
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Summary:High-order variational models have the ability to remove the staircase effect generated by the total variation regularizer. They, however, tend to blur object edges. To overcome this drawback, we introduce an edge coherence-weighted second-order (ECSO) model for image denoising. We propose novel regularizers that use the edge coherence quantity to adjust the strength of regularization according to the characteristics of each pixel. We then adapt the split Bregman algorithm to solve the proposed model. All the subproblems are solved efficiently using the fast Fourier transform and the shrinkage operator. Extensive experiments show that the proposed model outperforms state-of-the-art high-order variational models for image denoising.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-022-02209-z