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| Improved K-SVD image denoising algorithm |
| CHENG Yi-feng,LIU Zeng-li |
| Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, China |
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Abstract Aiming at the problem of shortage of signal utilization by traditional K-SVD. Using sparse Bayesian learning to preprocess the image signal. Combining the orthogonal matching pursuit algorithm with the improved steepest descent algorithm. Taking into account the noise atoms are present in the dictionary after the update of the dictionary, so combined with the Bartlett test method to cut off the noise atoms. Experimental results show that the method is better than the wavelet threshold denoising algorithm and the sparse representation based on DCT dictionary. Also, the method can remove the noise better, preserve image edge information, obtain higher peak signal to noise ratio, and the resulting image has a better visual effect.
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Received: 31 August 2016
Published: 12 April 2018
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［1］Mallat S G, ZHANG Z F. Matching pursuit with time-frequency dictionary［J］. IEEE Transactions on Signal Processing, 1993, 41(12):3397-3415.
［2］Aharon M, Elad M, Bruckstrein A M. The K-SVD: an algorithm for designing of over-complete dictionaries for sparse representation［J］. IEEE Transaction on Image Processing, 2006, 54(11): 4311-4322.
［3］Wipf D P, Rao B D. Sparse Bayesian learning for basisselection［J］. Signal Processing IEEE Trans on, 2004, 52,(8):2153-2164.
［4］Bartlett M S. Properties of Sufficiency and Statistical Tests［J］.Proceedings of the Royal Society of London. Series A,Mathematical andPhysical Sciences,1937, 160(901):268-282.
［5］蒋帅. K-均值聚类算法研究［D］. 西安:陕西师范大学, 2010.
［6］田源,王洪涛. 基于量子核聚类算法的图像边缘特征提取研究［J］. 计量学报,2016,37(6):582-586.
［7］ZHOU F, LI L. Modified sparse Bayesian dictionary learning K-SVD algorithm for video image sparse representation［J］. Journal of Computational Information Systems, 2015, 11(12):4567-4580.
［8］Lee J, Kim Y H, Nam J H. Adaptive noise reduction algorithms based on statistical hypotheses tests［J］. IEEE Transactions on Consumer Electronics, 2008, 54(3):1406-1414.
［9］龙建辉, 胡锡炎, 张磊. 最速下降法解二次矩阵方程［J］. 湖南大学学报(自然科学版), 2008, 54(3):85-88.
［10］郑慧峰,喻桑桑,王月兵,等. 基于经验模态分解和奇异值分解的振动声调制信号分析方法研究［J］. 计量学报,2016,37(4):398-401.
［11］周灿梅. 基于压缩感知的信号重建算法研究［D］.北京:北京交通大学, 2010. |
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2018, Vol. 39 
