Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163340
Title: Single image super resolution
Authors: Chan, Jeremiah Sheng En
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chan, J. S. E. (2022). Single image super resolution. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163340
Project: SCSE21-0823
Abstract: In 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.
URI: https://hdl.handle.net/10356/163340
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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