Investigation of Deconvolution Method with Adaptive Point Spread Function Based on Scintillator Thickness in Wavelet Domain
DC Field | Value | Language |
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dc.contributor.author | Kim, Kyuseok | - |
dc.contributor.author | Cha, Bo Kyung | - |
dc.contributor.author | Jeong, Hyun-Woo | - |
dc.contributor.author | Lee, Youngjin | - |
dc.date.accessioned | 2024-06-04T06:30:28Z | - |
dc.date.available | 2024-06-04T06:30:28Z | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 2306-5354 | - |
dc.identifier.issn | 2306-5354 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91415 | - |
dc.description.abstract | In recent years, indirect digital radiography detectors have been actively studied to improve radiographic image performance with low radiation exposure. This study aimed to achieve low-dose radiation imaging with a thick scintillation detector while simultaneously obtaining the resolution of a thin scintillation detector. The proposed method was used to predict the optimal point spread function (PSF) between thin and thick scintillation detectors by considering image quality assessment (IQA). The process of identifying the optimal PSF was performed on each sub-band in the wavelet domain to improve restoration accuracy. In the experiments, the edge preservation index (EPI) values of the non-blind deblurred image with a blurring sigma of sigma = 5.13 pixels and the image obtained with optimal parameters from the thick scintillator using the proposed method were approximately 0.62 and 0.76, respectively. The coefficient of variation (COV) values for the two images were approximately 1.02 and 0.63, respectively. The proposed method was validated through simulations and experimental results, and its viability is expected to be verified on various radiological imaging systems. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Investigation of Deconvolution Method with Adaptive Point Spread Function Based on Scintillator Thickness in Wavelet Domain | - |
dc.type | Article | - |
dc.identifier.wosid | 001210037600001 | - |
dc.identifier.doi | 10.3390/bioengineering11040330 | - |
dc.identifier.bibliographicCitation | BIOENGINEERING-BASEL, v.11, no.4 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85191576204 | - |
dc.citation.title | BIOENGINEERING-BASEL | - |
dc.citation.volume | 11 | - |
dc.citation.number | 4 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | image deblurring | - |
dc.subject.keywordAuthor | adaptive point spread function (PSF) | - |
dc.subject.keywordAuthor | wavelet transform | - |
dc.subject.keywordAuthor | image quality assessment (IQA) | - |
dc.subject.keywordAuthor | radiography | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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