Computer Science ›› 2018, Vol. 45 ›› Issue (2): 94-97, 134.doi: 10.11896/j.issn.1002-137X.2018.02.016

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Sparsity-adaptive Image Denoising Algorithm Based on Difference Coefficient

JIAO Li-juan and WANG Wen-jian   

  • Online:2018-02-15 Published:2018-11-13

Abstract: With the remarkable adaptability and the details recovery capability,K-SVD is a highly effective method based on sparse representation theory in image denoising.But the sparsity K should be given in advance,and different images have different K values in fact.On the other hand,pursuit algorithms which are used in evaluating the relevance between vectors of an image by calculating vector inner product,are brought into K-SVD to train sparse coefficients.Denoising effect is reduced because a few noisy pixels are likely to cause false relevance.This paper addressed this problem and proposed a novel sparsity-adaptive speeded K-SVD(SASK-SVD) algorithm based on different coefficient,which can improve the efficiency.The different coefficient is to eliminate false relevance.The sparsity K is adaptively generated by using the average correlation as the threshold.This study conducted extensive experiments to demonstrate these ideas.The experimental results show that the proposed method achieves the state-of-the-art denoising performance.

Key words: Image denoising,K-means singular value decomposition,Sparsity-adaptive,Difference coefficient

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