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 |