Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106614
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Lu, J | en_US |
dc.creator | Huang, L | en_US |
dc.creator | Liu, X | en_US |
dc.creator | Xie, NX | en_US |
dc.creator | Jiang, Q | en_US |
dc.creator | Zou, Y | en_US |
dc.date.accessioned | 2024-05-17T06:04:39Z | - |
dc.date.available | 2024-05-17T06:04:39Z | - |
dc.identifier.issn | 0266-5611 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106614 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Physics Publishing Ltd. | en_US |
dc.rights | © 2024 The Author(s). Published by IOP Publishing Ltd | en_US |
dc.rights | Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | en_US |
dc.rights | The following publication Lu, J., Huang, L., Liu, X., Xie, N., Jiang, Q., & Zou, Y. (2024). 3D Poissonian image deblurring via patch-based tensor logarithmic Schatten-p minimization. Inverse Problems, 40(6), 065010 is available at https://doi.org/10.1088/1361-6420/ad40c9. | en_US |
dc.subject | Deblurring | en_US |
dc.subject | Non-local low-rank regularization | en_US |
dc.subject | Poisson noise | en_US |
dc.subject | Tensor low-rank measure | en_US |
dc.title | 3D Poissonian image deblurring via patch-based tensor logarithmic Schatten-p minimization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 40 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.doi | 10.1088/1361-6420/ad40c9 | en_US |
dcterms.abstract | In medical and biological image processing, multi-dimensional images are often corrupted by blur and Poisson noise. In this paper, we first propose a new tensor logarithmic Schatten-p (t-log-Sp) low-rank measure and a tensor iteratively reweighted Schatten-p minimization algorithm for minimizing such measure. Furthermore, we adopt this low-rank measure to regularize the non-local tensors formed by similar 3D image patches and develop a patch-based non-local low-rank model. The data fidelity term of the model characterizes the Poisson noise distribution and blur operator. The optimization model is further solved by an alternating minimization technique combined with variable splitting. Experimental results tested on 3D fluorescence microscope images show that the proposed patch-based tensor logarithmic Schatten-p minimization method outperforms state-of-the-art methods in terms of image evaluation metrics and visual quality. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Inverse problems, June 2024, v. 40, no. 6, 065010 | en_US |
dcterms.isPartOf | Inverse problems | en_US |
dcterms.issued | 2024-06 | - |
dc.identifier.eissn | 1361-6420 | en_US |
dc.identifier.artn | 65010 | en_US |
dc.description.validate | 202405 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_TA | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province of China; Educational Commission of Guangdong Province of China; Shenzhen Basis Research Project; PolyU internal Grant | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.TA | IOP (2024) | en_US |
dc.description.oaCategory | TA | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lu_2024_Inverse_Problems_40_065010.pdf | 2.35 MB | Adobe PDF | View/Open |
Page views
17
Citations as of Jun 30, 2024
Downloads
7
Citations as of Jun 30, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.