Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168449
Title: An AI-based image enhancement system with its FPGA implementation
Authors: Yang, Hao
Keywords: Engineering::Electrical and electronic engineering::Integrated circuits
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Yang, H. (2023). An AI-based image enhancement system with its FPGA implementation. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/168449
Abstract: One of the essential parts of image processing is image enhancement. With the contribution of artificial intelligence (AI), this dissertation proposes a novel deep learning system for image enhancement. The proposed network is based on the structure of U-Net, and it is capable of image enhancing and denoising simultaneously. The experiment results of this system show a significant performance improvement compared to conventional systems in adaptive methods. By introducing pixel shuffle algorithms from super-resolution, we eliminate checkerboard artifacts significantly. Finally, the proposed network achieves a quantitative evaluation with PSNR/SSIM is 20/0.85 with the post-trained model. This proposed system could be implemented with the Xilinx FPGA platform. Furthermore, an FPGA platform that runs NVDLA as a hardware backend has been implemented and tested with Lenet5 and Resnet18.
URI: https://hdl.handle.net/10356/168449
Schools: School of Electrical and Electronic Engineering 
Organisations: Reexen Technology
Technical University of Munich
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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