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- W2591754912 abstract "The images obtained by observing the sun through a large telescope always suffered with noise due to the low SNR. K-SVD denoising algorithm can effectively remove Gauss white noise. Training dictionaries for sparse representations is a time consuming task, due to the large size of the data involved and to the complexity of the training algorithms. In this paper, an OpenMP parallel programming language is proposed to transform the serial algorithm to the parallel version. Data parallelism model is used to transform the algorithm. Not one atom but multiple atoms updated simultaneously is the biggest change. The denoising effect and acceleration performance are tested after completion of the parallel algorithm. Speedup of the program is 13.563 in condition of using 16 cores. This parallel version can fully utilize the multi-core CPU hardware resources, greatly reduce running time and easily to transplant in multi-core platform." @default.
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- W2591754912 date "2017-02-27" @default.
- W2591754912 modified "2023-09-23" @default.
- W2591754912 title "Parallel transformation of K-SVD solar image denoising algorithm" @default.
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- W2591754912 doi "https://doi.org/10.1117/12.2256495" @default.
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