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Cited by 2Advanced Image Processing TechniquesImage and Signal Denoising MethodsImage Processing Techniques and Applications

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Blind Deconvolution with Non-local Similarity and $l_0$ Sparsity for Noisy Image

Ren, Weihong, Tian, Jiandong, Tang, Yandong·2016·IEEE Signal Processing Letters
DOI 10.1109/lsp.2016.2530855
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Abstract

The blind image deconvolution techniques with sparsity prior in gradient domain are sensitive to noise, even a small amount of noise. To address this problem, in this letter, we propose a novel blind deconvolution model that combines low-rank property, nonlocal similarity, and l0sparsity prior. Low-rank property makes the proposed deblurring model robust to image noise. The joint utilization of nonlocal similarity and l0sparsity prior has improved the accuracy of blur kernel estimation and restores the fine image details. A numerical method is also given to solve the proposed problem. Experimental results on synthetic and real data show that our algorithm performs better against with the state-of-the-art methods for both noise and noise-free images.

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Research works printed but not linked: 22

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Metadata

Authors
Ren, W., Tian, J., & Tang, Y.
Affiliations
Shenyang Institute of Automation (CN)
ORCID
Venue
IEEE Signal Processing Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016
Pages
1-1
ISSN
  • 1070-9908 (ISSN-L)
  • 1558-2361
DOI
10.1109/lsp.2016.2530855
OpenAlex
W2334453399
Cited by
2
References
0
Citations by year
2020: 2
FWCI
0.00
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Signal Processing Letters (publishedVersion)
  • SIA OpenIR (Chinese Academy of Sciences) (submittedVersion)
Funders
National Natural Science Foundation of China (61333019, 61473280)
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, image deblurring, nonlocal similarity, low-rank prior, blur kernel estimation, noise robustness, gradient domain, image restoration

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CoChat. (2026). Citation check: Blind Deconvolution with Non-local Similarity and $l_0$ Sparsity for Noisy Image. https://app.cochat.ai/papers/10.1109/lsp.2016.2530855
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Ren, W., Tian, J., & Tang, Y. (2016). Blind Deconvolution with Non-local Similarity and $l_0$ Sparsity for Noisy Image. IEEE Signal Processing Letters. https://doi.org/10.1109/lsp.2016.2530855