Blind Image Deblurring via Reweighted Graph Total Variation Article Swipe
Yuanchao Bai
,
Gene Cheung
,
Xianming Liu
,
Wen Gao
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1712.08877
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1712.08877
Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, de-convolve blurry input to restore the target image. In this paper, by interpreting an image patch as a signal on a weighted graph, we first argue that a skeleton image---a proxy that retains the strong gradients of the target but smooths out the details---can be used to accurately estimate the blur kernel and has a unique bi-modal edge weight distribution. We then design a reweighted graph total variation (RGTV) prior that can efficiently promote bi-modal edge weight distribution given a blurry patch. However, minimizing a blind image deblurring objective with RGTV results in a non-convex non-differentiable optimization problem. We propose a fast algorithm that solves for the skeleton image and the blur kernel alternately. Finally with the computed blur kernel, recent non-blind image deblurring algorithms can be applied to restore the target image. Experimental results show that our algorithm can robustly estimate the blur kernel with large kernel size, and the reconstructed sharp image is competitive against the state-of-the-art methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1712.08877
- https://arxiv.org/pdf/1712.08877
- OA Status
- green
- Cited By
- 1
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2776840758
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- OpenAlex ID
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https://openalex.org/W2776840758Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1712.08877Digital Object Identifier
- Title
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Blind Image Deblurring via Reweighted Graph Total VariationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-12-24Full publication date if available
- Authors
-
Yuanchao Bai, Gene Cheung, Xianming Liu, Wen GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/1712.08877Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1712.08877Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1712.08877Direct OA link when available
- Concepts
-
Deblurring, Kernel (algebra), Artificial intelligence, Kernel density estimation, Mathematics, Image restoration, Computer vision, Image (mathematics), Computer science, Pattern recognition (psychology), Image processing, Statistics, Combinatorics, EstimatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2018: 1Per-year citation counts (last 5 years)
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16Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.knowledge | 6 |
| abstract_inverted_index.non-blind | 160 |
| abstract_inverted_index.objective | 127 |
| abstract_inverted_index.variation | 106 |
| abstract_inverted_index.accurately | 86 |
| abstract_inverted_index.algorithms | 163 |
| abstract_inverted_index.deblurring | 4, 126, 162 |
| abstract_inverted_index.minimizing | 122 |
| abstract_inverted_index.non-convex | 133 |
| abstract_inverted_index.reweighted | 103 |
| abstract_inverted_index.competitive | 194 |
| abstract_inverted_index.de-convolve | 39 |
| abstract_inverted_index.deblurring, | 2 |
| abstract_inverted_index.efficiently | 111 |
| abstract_inverted_index.Experimental | 172 |
| abstract_inverted_index.alternately. | 152 |
| abstract_inverted_index.distribution | 116 |
| abstract_inverted_index.interpreting | 51 |
| abstract_inverted_index.optimization | 135 |
| abstract_inverted_index.details---can | 82 |
| abstract_inverted_index.distribution. | 98 |
| abstract_inverted_index.reconstructed | 190 |
| abstract_inverted_index.state-of-the-art | 197 |
| abstract_inverted_index.non-differentiable | 134 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |