The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Variational Bayesian Sparse Kernel-Based Blind Image Deconvolution With Student's-t Priors
Abstract
In this paper, we present a new Bayesian model for the blind image deconvolution (BID) problem. The main novelty of this model is the use of a sparse kernel-based model for the point spread function (PSF) that allows estimation of both PSF shape and support. In the herein proposed approach, a robust model of the BID errors and an image prior that preserves edges of the reconstructed image are also used. Sparseness, robustness, and preservation of edges are achieved by using priors that are based on the Student's-t probability density function (PDF). This pdf, in addition to having heavy tails, is closely related to the Gaussian and, thus, yields tractable inference algorithms. The approximate variational inference methodology is used to solve the corresponding Bayesian model. Numerical experiments are presented that compare this BID methodology to previous ones using both simulated and real data.
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“The first method based on the probabilistic Bayes model is the Variational Bayes method [21, 22], which estimates the mean posteriori values.”
From A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring (2020) · cites this paper as [22]
In the citing paper
“This method is effective in accurate PSF estimation for various types of blurs such as motion blur, out-of-focus blur in various images”
From A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring (2020) · cites this paper as [22]
In the citing paper
“NP-Hard Problem”
From A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring (2020) · cites this paper as [22]
In the citing paper
“preserves the structure”
From A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring (2020) · cites this paper as [22]
Citations
Cites
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Cited by
Sorted by citations
A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring
Satish, P., Srikantaswamy, M., & Ramaswamy, N. (2020) · Traitement du signal · doi:10.18280/ts.370321 · Cited by 59
Metadata
- Authors
- Tzikas, D. G., Likas, A. C., & Galatsanos, N. P.
- Affiliations
- University of Ioannina (GR)
- University of Patras (GR)
- ORCID
- Likas, A.C.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2009-04
- Volume
- 18
- Issue
- 4
- Pages
- 753-764
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2128104012
- Cited by
- 80
- References
- 2
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2022: 5 · 2020: 3 · 2019: 2 · 2018: 3 · 2017: 6 · 2016: 6 · 2015: 9 · 2014: 12 · 2013: 8 · 2012: 8
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- blind image deconvolution, sparse kernels, point spread function estimation, heavy-tailed priors, variational inference, edge preservation, sparse priors
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