An AI-based image enhancement system with its FPGA implementation
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...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/168449 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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