Hessian-based weighted guided image filtering Article Swipe
YOU?
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· 2022
· Open Access
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· DOI: https://doi.org/10.21203/rs.3.rs-1892961/v1
Guided image filtering (GIF) is a popular edge-preserving smoothing technique, and the regularization parameter selection plays an important role in the performance of GIF. In this paper, we propose a new guided image filter based on Hessian matrix which consists of the second-order derivatives of an image. More specifically, a new structural measurement index is introduced by using the eigenvalues of the Hessian matrix first, which can distinguish the texture and flat regions of the image. Then the regularization parameter is adjusted based on this Hessian-based second-order structure measurement index, that is, a large regularization parameter is selected to improve the smoothness of the flat regions, while a small regularization parameter is set for the texture regions to preserve the image structure such as edges and corners. To further improve the quality of the filtered images, we also introduce a weighted averaging technique to the linear filter coefficients based on local variance. Experimental results show that the proposed Hessian-based weighted guided image filtering (HWGIF) method outperforms the state-of-the-art approaches in image processing applications such as edge-preserving denoising, detail enhancement, dehazing and HDR compression.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1892961/v1
- https://www.researchsquare.com/article/rs-1892961/latest.pdf
- OA Status
- gold
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289667367
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4289667367Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-1892961/v1Digital Object Identifier
- Title
-
Hessian-based weighted guided image filteringWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-03Full publication date if available
- Authors
-
Jiaxin Wu, Shoulie Xie, Wei Cao, Shiqian WuList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1892961/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-1892961/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-1892961/latest.pdfDirect OA link when available
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Hessian matrix, Smoothing, Regularization (linguistics), Artificial intelligence, Mathematics, Computer science, Algorithm, Computer vision, Pattern recognition (psychology), Applied mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.filtered | 135 |
| abstract_inverted_index.preserve | 119 |
| abstract_inverted_index.proposed | 158 |
| abstract_inverted_index.regions, | 106 |
| abstract_inverted_index.selected | 98 |
| abstract_inverted_index.weighted | 141, 160 |
| abstract_inverted_index.averaging | 142 |
| abstract_inverted_index.filtering | 3, 163 |
| abstract_inverted_index.important | 18 |
| abstract_inverted_index.introduce | 139 |
| abstract_inverted_index.parameter | 14, 80, 96, 111 |
| abstract_inverted_index.selection | 15 |
| abstract_inverted_index.smoothing | 9 |
| abstract_inverted_index.structure | 88, 122 |
| abstract_inverted_index.technique | 143 |
| abstract_inverted_index.variance. | 152 |
| abstract_inverted_index.approaches | 169 |
| abstract_inverted_index.denoising, | 177 |
| abstract_inverted_index.introduced | 56 |
| abstract_inverted_index.processing | 172 |
| abstract_inverted_index.smoothness | 102 |
| abstract_inverted_index.structural | 52 |
| abstract_inverted_index.technique, | 10 |
| abstract_inverted_index.derivatives | 44 |
| abstract_inverted_index.distinguish | 68 |
| abstract_inverted_index.eigenvalues | 60 |
| abstract_inverted_index.measurement | 53, 89 |
| abstract_inverted_index.outperforms | 166 |
| abstract_inverted_index.performance | 22 |
| abstract_inverted_index.Experimental | 153 |
| abstract_inverted_index.applications | 173 |
| abstract_inverted_index.coefficients | 148 |
| abstract_inverted_index.compression. | 183 |
| abstract_inverted_index.enhancement, | 179 |
| abstract_inverted_index.second-order | 43, 87 |
| abstract_inverted_index.Hessian-based | 86, 159 |
| abstract_inverted_index.specifically, | 49 |
| abstract_inverted_index.regularization | 13, 79, 95, 110 |
| abstract_inverted_index.edge-preserving | 8, 176 |
| abstract_inverted_index.state-of-the-art | 168 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.08164373 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |