Gaussian Kernel Mixture Network for Single Image Defocus Deblurring Article Swipe
Yuhui Quan
,
Zicong Wu
,
Hui Ji
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.00454
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.00454
Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount. This paper presents an end-to-end deep learning approach for removing defocus blur from a single image, so as to have an all-in-focus image for consequent vision tasks. First, a pixel-wise Gaussian kernel mixture (GKM) model is proposed for representing spatially variant defocus blur kernels in an efficient linear parametric form, with higher accuracy than existing models. Then, a deep neural network called GKMNet is developed by unrolling a fixed-point iteration of the GKM-based deblurring. The GKMNet is built on a lightweight scale-recurrent architecture, with a scale-recurrent attention module for estimating the mixing coefficients in GKM for defocus deblurring. Extensive experiments show that the GKMNet not only noticeably outperforms existing defocus deblurring methods, but also has its advantages in terms of model complexity and computational efficiency.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.00454
- https://arxiv.org/pdf/2111.00454
- OA Status
- green
- Cited By
- 24
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3210346746
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3210346746Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2111.00454Digital Object Identifier
- Title
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Gaussian Kernel Mixture Network for Single Image Defocus DeblurringWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-10-31Full publication date if available
- Authors
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Yuhui Quan, Zicong Wu, Hui JiList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.00454Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2111.00454Direct 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
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https://arxiv.org/pdf/2111.00454Direct OA link when available
- Concepts
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Deblurring, Kernel (algebra), Computer science, Artificial intelligence, Image restoration, Computer vision, Focus (optics), Deep learning, Gaussian, Image (mathematics), Mathematics, Image processing, Optics, Combinatorics, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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24Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 8, 2023: 7, 2022: 2Per-year citation counts (last 5 years)
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.has | 137 |
| abstract_inverted_index.its | 19, 138 |
| abstract_inverted_index.not | 127 |
| abstract_inverted_index.one | 3 |
| abstract_inverted_index.the | 94, 113, 125 |
| abstract_inverted_index.This | 23 |
| abstract_inverted_index.also | 136 |
| abstract_inverted_index.blur | 1, 6, 34, 65 |
| abstract_inverted_index.deep | 28, 81 |
| abstract_inverted_index.from | 35 |
| abstract_inverted_index.have | 42 |
| abstract_inverted_index.kind | 4 |
| abstract_inverted_index.only | 128 |
| abstract_inverted_index.seen | 9 |
| abstract_inverted_index.show | 123 |
| abstract_inverted_index.than | 76 |
| abstract_inverted_index.that | 124 |
| abstract_inverted_index.with | 73, 106 |
| abstract_inverted_index.(GKM) | 56 |
| abstract_inverted_index.Then, | 79 |
| abstract_inverted_index.built | 100 |
| abstract_inverted_index.form, | 72 |
| abstract_inverted_index.image | 45 |
| abstract_inverted_index.model | 57, 143 |
| abstract_inverted_index.often | 8 |
| abstract_inverted_index.paper | 24 |
| abstract_inverted_index.terms | 141 |
| abstract_inverted_index.which | 12 |
| abstract_inverted_index.First, | 50 |
| abstract_inverted_index.GKMNet | 85, 98, 126 |
| abstract_inverted_index.called | 84 |
| abstract_inverted_index.higher | 74 |
| abstract_inverted_index.image, | 38 |
| abstract_inverted_index.kernel | 54 |
| abstract_inverted_index.linear | 70 |
| abstract_inverted_index.mixing | 114 |
| abstract_inverted_index.module | 110 |
| abstract_inverted_index.neural | 82 |
| abstract_inverted_index.remove | 16 |
| abstract_inverted_index.single | 37 |
| abstract_inverted_index.tasks. | 49 |
| abstract_inverted_index.vision | 48 |
| abstract_inverted_index.Defocus | 0 |
| abstract_inverted_index.amount. | 22 |
| abstract_inverted_index.defocus | 33, 64, 119, 132 |
| abstract_inverted_index.effects | 7 |
| abstract_inverted_index.images, | 11 |
| abstract_inverted_index.kernels | 66 |
| abstract_inverted_index.mixture | 55 |
| abstract_inverted_index.models. | 78 |
| abstract_inverted_index.network | 83 |
| abstract_inverted_index.variant | 21, 63 |
| abstract_inverted_index.Gaussian | 53 |
| abstract_inverted_index.accuracy | 75 |
| abstract_inverted_index.approach | 30 |
| abstract_inverted_index.existing | 77, 131 |
| abstract_inverted_index.learning | 29 |
| abstract_inverted_index.methods, | 134 |
| abstract_inverted_index.presents | 25 |
| abstract_inverted_index.proposed | 59 |
| abstract_inverted_index.removing | 32 |
| abstract_inverted_index.Extensive | 121 |
| abstract_inverted_index.GKM-based | 95 |
| abstract_inverted_index.attention | 109 |
| abstract_inverted_index.developed | 87 |
| abstract_inverted_index.efficient | 69 |
| abstract_inverted_index.iteration | 92 |
| abstract_inverted_index.spatially | 20, 62 |
| abstract_inverted_index.unrolling | 89 |
| abstract_inverted_index.advantages | 139 |
| abstract_inverted_index.complexity | 144 |
| abstract_inverted_index.consequent | 47 |
| abstract_inverted_index.deblurring | 133 |
| abstract_inverted_index.end-to-end | 27 |
| abstract_inverted_index.estimating | 112 |
| abstract_inverted_index.noticeably | 129 |
| abstract_inverted_index.parametric | 71 |
| abstract_inverted_index.pixel-wise | 52 |
| abstract_inverted_index.challenging | 14 |
| abstract_inverted_index.deblurring. | 96, 120 |
| abstract_inverted_index.efficiency. | 147 |
| abstract_inverted_index.experiments | 122 |
| abstract_inverted_index.fixed-point | 91 |
| abstract_inverted_index.lightweight | 103 |
| abstract_inverted_index.outperforms | 130 |
| abstract_inverted_index.all-in-focus | 44 |
| abstract_inverted_index.coefficients | 115 |
| abstract_inverted_index.representing | 61 |
| abstract_inverted_index.architecture, | 105 |
| abstract_inverted_index.computational | 146 |
| abstract_inverted_index.scale-recurrent | 104, 108 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |