A Novel Algorithm of GPU Acceleration for “Super Resolution from a Single Image” Using Patch Characteristic Hashing
碩士 === 國立臺灣大學 === 電子工程學研究所 === 101 === Abstract Super resolution imaging is the technology of reconstructing high resolution images with high frequency details from low resolution images recorded by cameras. The needs for high image resolution stem from two application areas: (1) improvement of...
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| Format: | Others |
| Language: | en_US |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/94760732681315377755 |
| Summary: | 碩士 === 國立臺灣大學 === 電子工程學研究所 === 101 === Abstract
Super resolution imaging is the technology of reconstructing high resolution images with high frequency details from low resolution images recorded by cameras. The needs for high image resolution stem from two application areas: (1) improvement of pictorial information for interpretation; (2) helping representation for automatic machine perception.
“Super Resolution from a Single Image” proposed by Glasner is the most promising method among various super resolution approaches, but its computation time is very long due to high dimensional k-nearest-neighbor search. We proposed a novel patch characteristic hashing method with GPU accelerating k-nearest neighbor search to speed-up the process. Our system is implemented on MATLAB, and we use CUDA C to implement KNN search. The proposed architecture is tested with Berkeley Segmentation Dataset and Benchmark.
The results show that our method can speed-up “Super Resolution from a Single Image” by 150 times faster. The average PSNR is only 0.038dB lower and Structural Similarity (SSIM) only drops by 0.009. The results implicate that the proposed patch characteristic hashing (PCH) can accelerate “Super Resolution from a Single Image” without affecting output quality of the reconstructed images.
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