Magnetic resonance image is an important research field in medical image processing. Because it can degrade the image quality, the signal noises have a negative impact on clinical diagnosis. The K-SVD algorithm can obtain better de-noising results, but the time-consuming problem of the dictionary training still exists. A medical image denoising algorithm based on improved K-SVD is studied to solve this problem. First of all, an efficient and flexible dictionary structure is proposed based on a sparsity model of the dictionary atoms over a know dictionary. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. Then the basic framework of the existing K-SVD algorithm, combined with the dictionary sparse representation, can improve K-SVD training algorithm, and the improved K-SVD algorithm can be trained for greater dictionary, especially for high-dimensional data. Therefore, it can be used to remove the noise of magnetic resonance images. The experimental results show that the algorithm, in comparison with the discrete cosine transform dictionary and conventional K-SVD algorithm, can effectively filter Gaussian white noise of the image to retain image details, and reduce the time of dictionary training. It is found that the peak signal-to-noise ratio is increased by about 1~3db with the proposed method.
JIANG Xingguo
,
QIN Yang
,
WEI Baolin
. A Magnetic Resonance Image De-noising Approach Based on Improved K-SVD[J]. Science & Technology Review, 2014
, 32(8)
: 64
-69
.
DOI: 10.3981/j.issn.1000-7857.2014.08.010
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