Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163340
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dc.contributor.authorChan, Jeremiah Sheng Enen_US
dc.date.accessioned2022-12-05T00:40:02Z-
dc.date.available2022-12-05T00:40:02Z-
dc.date.issued2022-
dc.identifier.citationChan, J. S. E. (2022). Single image super resolution. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163340en_US
dc.identifier.urihttps://hdl.handle.net/10356/163340-
dc.description.abstractIn the field of computer vision, super resolution with deep learning is a promising field that has generated multiple research, and has seen its application far and wide. In single image super resolution, the image can either be upscaled before being input into the network (pre upscaling) and its features learned, or it can be upscaled after the network has learned its feature (post upscaling). As with any deep learning models, the inputs into the model can affect the outputs produced. Hence, the goal of this project is to find out how much we can improve a super resolution model by filtering the inputs using a classification model. In this report, we will be discussing our analysis of the data, methodology and models used for classification/super resolution, and the results produced from our experiments. We will also be discussing some of the limitations of the project and future work regarding this project.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE21-0823en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titleSingle image super resolutionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorChen Change Loyen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailccloy@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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