Algorithm of Noise Reduction for Adaptive Dictionary Learning Research Based on Brain MRI Images

Abstract:

Recent years, the image sparse representation has been the popular method in the study of image representation, which has put forward a new idea in the image denoising. Its basic principle is that the original image has the sparse representation under the proper over-complete dictionary. Filter out the noise, we should find out the sparse representation of the image through the design of the dictionary. Its mechanism is that one hand the useful information of the image would be effectively expressed because of the sparse decomposition algorithm based on the redundant dictionary. The other the noise would not be expressed through the dictionary atoms. We do the image denoising according to the image sparse representation. Because of the superiority of the adaptive dictionary algorithm in the image, in this paper, we discuss the over-complete dictionary training algorithm. And we prove the effectiveness through the MATLAB.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 4123-4127
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.4123
• Online since: November 2014
• Authors: Jia Qing Miao
• Keywords: Dictionary Learning, Image Denoising, KSVD Dictionary, MRI, Sparse Representation

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