The Implementation of Parallelization for The Time-Lapse Fusion Algorithm
碩士 === 輔仁大學 === 電機工程學系碩士班 === 104 === Time-Lapse Fusion(TLF) is an image sequence blending algorithm which can create high dynamic range image sequences. Through a temporally fading mechanism, the algorithm generates a long exposure effect. However, the computational loading of TLF is tremendous due...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2016
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| Online Access: | http://ndltd.ncl.edu.tw/handle/03819176528368540277 |
| Summary: | 碩士 === 輔仁大學 === 電機工程學系碩士班 === 104 === Time-Lapse Fusion(TLF) is an image sequence blending algorithm which can create high dynamic range image sequences. Through a temporally fading mechanism, the algorithm generates a long exposure effect. However, the computational loading of TLF is tremendous due to its repeated computational pattern with a single program multiple data (SPMD) fashion. In this paper we devise a parallel TLF method called GTLF using GPU and OpenCL to parallelize the serial TLF. The GTLF uses OpenCL kernel under the GPU architecture and improves the efficiency by memory hierarchy. A computational analysis of the GTLF is discussed in details in this paper. We compare the run time and speedup between the GTLF and the serial TLF called CTLF. The experiments of this paper are conducted on the GPU TeslaC2050 and the OpenCL 1.1 in OpenCV OCL module. Experimental results show that the GTLF can achieve 71 times speedup compared with the optimized single-threaded CTLF in image resolution 4096 x 4096, and parallel efficiency with 54%. Our experimental results demonstrate that using GPU and OpenCL can greatly improve the performance of the TLF algorithm.
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