Effective Five Directional Partial Derivatives-Based Image Smoothing and a Parallel Structure Design
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Choongsang | - |
dc.contributor.author | Lee, Sangkeun | - |
dc.date.accessioned | 2023-03-08T17:46:16Z | - |
dc.date.available | 2023-03-08T17:46:16Z | - |
dc.date.issued | 2016-04 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.issn | 1941-0042 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/64286 | - |
dc.description.abstract | Image smoothing has been used for image segmentation, image reconstruction, object classification, and 3D content generation. Several smoothing approaches have been used at the pre-processing step to retain the critical edge, while removing noise and small details. However, they have limited performance, especially in removing small details and smoothing discrete regions. Therefore, to provide fast and accurate smoothing, we propose an effective scheme that uses a weighted combination of the gradient, Laplacian, and diagonal derivatives of a smoothed image. In addition, to reduce computational complexity, we designed and implemented a parallel processing structure for the proposed scheme on a graphics processing unit (GPU). For an objective evaluation of the smoothing performance, the images were linearly quantized into several layers to generate experimental images, and the quantized images were smoothed using several methods for reconstructing the smoothly changed shape and intensity of the original image. Experimental results showed that the proposed scheme has higher objective scores and better successful smoothing performance than similar schemes, while preserving and removing critical and trivial details, respectively. For computational complexity, the proposed smoothing scheme running on a GPU provided 18 and 16 times lower complexity than the proposed smoothing scheme running on a CPU and the L0-based smoothing scheme, respectively. In addition, a simple noise reduction test was conducted to show the characteristics of the proposed approach; it reported that the presented algorithm outperforms the state-of-the art algorithms by more than 5.4 dB. Therefore, we believe that the proposed scheme can be a useful tool for efficient image smoothing. | - |
dc.format.extent | 9 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Effective Five Directional Partial Derivatives-Based Image Smoothing and a Parallel Structure Design | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TIP.2016.2526785 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON IMAGE PROCESSING, v.25, no.4, pp 1617 - 1625 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000384889300002 | - |
dc.identifier.scopusid | 2-s2.0-84963787956 | - |
dc.citation.endPage | 1625 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1617 | - |
dc.citation.title | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.citation.volume | 25 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Image smoothing | - |
dc.subject.keywordAuthor | Laplacian | - |
dc.subject.keywordAuthor | parallel processing | - |
dc.subject.keywordAuthor | low-complexity | - |
dc.subject.keywordPlus | REMOVAL | - |
dc.subject.keywordPlus | NOISE | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.