An Image De-Noising Algorithm Based on K-SVD and BM3D

Abstract:

The existence of noise affects the quality of the image seriously. The image de-noising algorithm based on KSVD appears fuzzy, where weak texture smooth area also can appear false textures, at the same time, when the noise was very big, the de-noising effect would not always be ideal. This paper proposed an image de-noising method based on K-SVD dictionary and BM3D. The algorithm can solve image weak texture fuzzy problems and weak edges effectively. The experimental results show that, compare with K-SVD de-noising algorithm, this algorithm has a good de-noising ability, which keeping the detail and the edge character of the image better.

• Periodical: Applied Mechanics and Materials (Volume 596)
• Pages: 333-336
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.596.333
• Online since: July 2014
• Authors: Jun Li
• Keywords: De-Nosing, Dictionary Learning, K-SVD, Sparse Representation

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