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dc.contributor.advisor Ambegoda T
dc.contributor.author Senivirathne BK
dc.date.accessioned 2022
dc.date.available 2022
dc.date.issued 2022
dc.identifier.citation Senivirathne, B.K. (2022). Single image super resolution with wide activation for mobile devices [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21591
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21591
dc.description.abstract Single Image Super Resolution (SISR) revolves around the task of reconstructing a high-resolution image from a single low-resolution image. Numerous applications of SISR range from surveillance & security, medical imaging to photographic utilities. Although there are ample SISR solutions, especially those which are deployed as cloud services, there’s a scarcity of effective on-device mobile SISR solutions. Even the existing solutions are mostly limited to high end mobile devices and most of the time limited by device architecture. An effective SISR solution which can run on any mobile device would be extremely helpful to the community in this context and can help gain a number of benefits in an edge-computing point of view, including storage and transfer optimization for image content. This research primarily focuses on creating such a solution, specifically focusing on usage of on-device Wide Attention Networks (WDSR) for SISR. In addition, a performance comparison will be done with other CNN based models. en_US
dc.language.iso en en_US
dc.subject SINGLE IMAGE SUPER RESOLUTION (SISR) en_US
dc.subject CNN en_US
dc.subject TENSORFLOW LITE en_US
dc.subject WDSR en_US
dc.subject INFORMATION TECHNOLOGY -Dissertation en_US
dc.subject COMPUTER SCIENCE -Dissertation en_US
dc.subject COMPUTER SCIENCE & ENGINEERING -Dissertation en_US
dc.title Single image super resolution with wide activation for mobile devices en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree MSc In Computer Science and Engineering en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.date.accept 2022
dc.identifier.accno TH4976 en_US


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