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• W3011448892 abstract "Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data." @default.
• W3011448892 created "2020-03-23" @default.
• W3011448892 creator A5037048332 @default.
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• W3011448892 date "2020-03-10" @default.
• W3011448892 modified "2023-09-26" @default.
• W3011448892 title "Non-Local SVD Denoising of MRI Based on Sparse Representations" @default.
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• W3011448892 doi "https://doi.org/10.3390/s20051536" @default.
• W3011448892 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7085762" @default.
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