Image Denoising Based on K-SVD with Adaptive Dictionary Size

Mei Ling Fu; Zhi Ming Wang; Rui Xin Cao

doi:10.4028/www.scientific.net/AMR.1049-1050.1645

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1049-1050Image Denoising Based on K-SVD with Adaptive...

Image Denoising Based on K-SVD with Adaptive Dictionary Size

Abstract:

Image denoising method based on K-SVD self-learning dictionary can effectively filter Gaussian white noise in an image and retain image details and texture information. This paper proposed an improved denoising algorithm which was based on K-SVD algorithm, but with adaptive dictionary size. Depending on the complexity of image content and the noise level, our algorithm determines the size of the dictionary adaptively. Experimental results show that proposed algorithm can reduce the number of entries of the dictionary significantly for the simple images, and increase the number of entries for the complex images. Both the efficiency and the denoising performance are improved compared with original K-SVD algorithm.

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Periodical:

Advanced Materials Research (Volumes 1049-1050)

Pages:

1645-1649

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1645

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Cite this paper

Online since:

October 2014

Authors:

Mei Ling Fu*, Zhi Ming Wang, Rui Xin Cao

Keywords:

Adaptive Dictionary, Image Denoising, K-SVD, Sparse Representation

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* - Corresponding Author

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