Image blending techniques based on GPU acceleration
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jung Soo | - |
dc.contributor.author | Lee, Min-Kyu | - |
dc.contributor.author | Chung, Ki Seok | - |
dc.date.accessioned | 2021-07-30T05:31:44Z | - |
dc.date.available | 2021-07-30T05:31:44Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2018-02 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/5317 | - |
dc.description.abstract | Today, image blending has been used for a high-resolution image in medical, aerospace, and even defense areas. To blend images, several filters and various processing steps such as Gaussian pyramid, Laplacian pyramid, and multi-band computation will be needed. However, these computations consist of a large amount of arithmetic operations. As the processing capability of graphic processing units (GPUs) grows very rapidly, GPUs have commonly been used to supplement central processing units (CPUs) for high-performance computing. By employing hardware accelerators such as GPU, a significant speedup can be achieved. In this paper, we present an implementation of fast image blending methods using compute unified device architecture (CUDA). The proposed implementation utilizes a shared memory in GPU better than conventional implementations leading to a better speed-up. The proposed implementation of this paper shows an improvement of 3.9 times in the overall execution time compared to a conventional implementation. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Image blending techniques based on GPU acceleration | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chung, Ki Seok | - |
dc.identifier.doi | 10.1145/3191442.3191471 | - |
dc.identifier.scopusid | 2-s2.0-85047317127 | - |
dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series, pp.106 - 109 | - |
dc.relation.isPartOf | ACM International Conference Proceeding Series | - |
dc.citation.title | ACM International Conference Proceeding Series | - |
dc.citation.startPage | 106 | - |
dc.citation.endPage | 109 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Computer graphics equipment | - |
dc.subject.keywordPlus | Image processing | - |
dc.subject.keywordPlus | Medical imaging | - |
dc.subject.keywordPlus | Memory architecture | - |
dc.subject.keywordPlus | Program processors | - |
dc.subject.keywordPlus | CUDA | - |
dc.subject.keywordPlus | GPGPU | - |
dc.subject.keywordPlus | Image blending | - |
dc.subject.keywordPlus | Image pyramids | - |
dc.subject.keywordPlus | Multi-band blending | - |
dc.subject.keywordPlus | Multi-resolution splines | - |
dc.subject.keywordPlus | Padding | - |
dc.subject.keywordPlus | Shared memory | - |
dc.subject.keywordPlus | Graphics processing unit | - |
dc.subject.keywordAuthor | CUDA | - |
dc.subject.keywordAuthor | GPGPU | - |
dc.subject.keywordAuthor | Image blending | - |
dc.subject.keywordAuthor | Image pyramid | - |
dc.subject.keywordAuthor | Multi-band blending | - |
dc.subject.keywordAuthor | Multi-resolution spline | - |
dc.subject.keywordAuthor | Padding | - |
dc.subject.keywordAuthor | Shared memory | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3191442.3191471 | - |
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