Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting Article Swipe
Haitao Hu
,
Hongmei Ma
,
Shuli Mei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.041416
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.041416
Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of the algorithm first. A novel deep wavelet dictionary is proposed that can better obtain image prior and possess learnable feature. And multi-layer deep sparse representation is used to implement dictionary learning, acquiring better signal expression. Compared with methods such as NLABH, Shearlet, Partial Differential Equation (PDE), K-Singular Value Decomposition (K-SVD), Convolutional Sparse Coding, and Deep Image Prior, the proposed algorithm has better subjective reconstruction and objective evaluation with small-scale image data, which realized high-fidelity inpainting, under the condition of small-scale image data. And the -level time complexity makes the proposed algorithm practical. The proposed algorithm can be effectively extended to other cross-sectional image inpainting problems, such as magnetic resonance images, and computed tomography images.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2023.041416
- https://file.techscience.com/files/cmc/2023/TSP_CMC-76-3/TSP_CMC_41416/TSP_CMC_41416.pdf
- OA Status
- diamond
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387436064
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387436064Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2023.041416Digital Object Identifier
- Title
-
Multi-Layer Deep Sparse Representation for Biological Slice Image InpaintingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Haitao Hu, Hongmei Ma, Shuli MeiList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2023.041416Publisher landing page
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https://file.techscience.com/files/cmc/2023/TSP_CMC-76-3/TSP_CMC_41416/TSP_CMC_41416.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://file.techscience.com/files/cmc/2023/TSP_CMC-76-3/TSP_CMC_41416/TSP_CMC_41416.pdfDirect OA link when available
- Concepts
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Inpainting, Artificial intelligence, Computer science, Pattern recognition (psychology), Iterative reconstruction, Deep learning, Sparse approximation, Convolutional neural network, Interpretability, Neural coding, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Coding, | 147 |
| abstract_inverted_index.Partial | 137 |
| abstract_inverted_index.between | 82 |
| abstract_inverted_index.factors | 31 |
| abstract_inverted_index.further | 78 |
| abstract_inverted_index.images, | 204 |
| abstract_inverted_index.images. | 50, 76, 208 |
| abstract_inverted_index.methods | 132 |
| abstract_inverted_index.possess | 112 |
| abstract_inverted_index.wavelet | 101 |
| abstract_inverted_index.(K-SVD), | 144 |
| abstract_inverted_index.Compared | 130 |
| abstract_inverted_index.Equation | 139 |
| abstract_inverted_index.However, | 17 |
| abstract_inverted_index.breakage | 47 |
| abstract_inverted_index.computed | 206 |
| abstract_inverted_index.ensuring | 90 |
| abstract_inverted_index.extended | 193 |
| abstract_inverted_index.feature. | 114 |
| abstract_inverted_index.magnetic | 202 |
| abstract_inverted_index.networks | 86 |
| abstract_inverted_index.presence | 27 |
| abstract_inverted_index.problems | 38 |
| abstract_inverted_index.proposed | 53, 104, 153, 184, 188 |
| abstract_inverted_index.realized | 167 |
| abstract_inverted_index.studying | 7 |
| abstract_inverted_index.systems. | 16 |
| abstract_inverted_index.Shearlet, | 136 |
| abstract_inverted_index.achieving | 70 |
| abstract_inverted_index.acquiring | 126 |
| abstract_inverted_index.algorithm | 61, 96, 154, 185, 189 |
| abstract_inverted_index.condition | 172 |
| abstract_inverted_index.discussed | 79 |
| abstract_inverted_index.effective | 4 |
| abstract_inverted_index.evolution | 12 |
| abstract_inverted_index.implement | 123 |
| abstract_inverted_index.learnable | 113 |
| abstract_inverted_index.learning, | 125 |
| abstract_inverted_index.mechanism | 13 |
| abstract_inverted_index.objective | 160 |
| abstract_inverted_index.preparing | 43 |
| abstract_inverted_index.problems, | 199 |
| abstract_inverted_index.resonance | 203 |
| abstract_inverted_index.structure | 10 |
| abstract_inverted_index.Biological | 0 |
| abstract_inverted_index.K-Singular | 141 |
| abstract_inverted_index.Therefore, | 51 |
| abstract_inverted_index.biological | 15, 55 |
| abstract_inverted_index.complexity | 21, 181 |
| abstract_inverted_index.corruption | 59 |
| abstract_inverted_index.dictionary | 102, 124 |
| abstract_inverted_index.difficulty | 41 |
| abstract_inverted_index.evaluation | 161 |
| abstract_inverted_index.inpainting | 60, 198 |
| abstract_inverted_index.practical. | 186 |
| abstract_inverted_index.subjective | 157 |
| abstract_inverted_index.technology | 24 |
| abstract_inverted_index.tomography | 207 |
| abstract_inverted_index.effectively | 192 |
| abstract_inverted_index.expression. | 129 |
| abstract_inverted_index.inpainting, | 169 |
| abstract_inverted_index.multi-layer | 66, 116 |
| abstract_inverted_index.preparation | 23, 34 |
| abstract_inverted_index.processing, | 35 |
| abstract_inverted_index.small-scale | 58, 163, 174 |
| abstract_inverted_index.Differential | 138 |
| abstract_inverted_index.relationship | 81 |
| abstract_inverted_index.Convolutional | 145 |
| abstract_inverted_index.Decomposition | 143 |
| abstract_inverted_index.convolutional | 84 |
| abstract_inverted_index.high-fidelity | 72, 92, 168 |
| abstract_inverted_index.physiological | 9 |
| abstract_inverted_index.characteristic | 93 |
| abstract_inverted_index.reconstruction | 73, 158 |
| abstract_inverted_index.representation | 119 |
| abstract_inverted_index.uncontrollable | 30 |
| abstract_inverted_index.cross-sectional | 196 |
| abstract_inverted_index.representation, | 69, 89 |
| abstract_inverted_index.interpretability | 63 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5115601345 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I52158045 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.20743713 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |