Application of Weighted Nuclear Norm Denoising Algorithm in Diffusion-Weighted Image
Diffusion-weighted imaging is a noninvasive method for detecting the diffusion of water molecules in living tissues, which requires high accuracy of the data. Diffusion-weighted images have a high degree of self-similarity. And it is widely used in the nerve fiber tracking in human brain. According to the characteristics of the image, the weighted nuclear norm denoising algorithm is proposed for the diffusion-weighted images denoising. By processing similar blocks of the image, the image can be reconstructed. The algorithm is compared with some traditional algorithms and we propose a new method to describe the denoising effect. The experiment shows that the weighted nuclear norm denoising algorithm makes good use of the self-similarity of the diffusion-weighted images, and achieves the purpose of image denoising through the processing of similar blocks. Can obtain better results from the proposed algorithm, the accuracy and validity of DTI data are improved, and it is very helpful for the subsequent processing of the image.
KeywordsDenoising Diffusion-weighted imaging Weighted nuclear norm denoising algorithm Nerve fiber tracking
This research is supported by the National Natural Science Foundation of China (Project No: 11265007).
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