کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4973572 1451646 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Denoising 3-D magnitude magnetic resonance images based on weighted nuclear norm minimization
ترجمه فارسی عنوان
محو کردن تصاویر 3 بعدی رزونانس مغناطیسی بر پایه کمینه کردن عناصر وزنی وزن
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
A new denoising algorithm based on low-rank matrix approximation (LRMA) with regularization of weighted nuclear norm minimization (WNNM) is proposed to remove Rician noise of magnetic resonance (MR) images. This technique simply groups similar non-local cubic blocks from noisy 3D MR data into a patch matrix with each block lexicographically vectorizing to be as a column, calculates the singular value decomposition (SVD) on this matrix, then the closed-form solution of LRMA is achieved by hard-thresholding different singular values with a different threshold. The denoised blocks are obtained from this estimate of the low-rank matrix, and the final estimate of the whole noise-free MR data is built up by aggregating all the denoised exemplar blocks that are overlapped each other. To further improve the denoising performance of the WNNM algorithm, we first realize the above denoising procedure in a two-iteration regularization framework, and then a simple non local means (NLM) filter based on single-pixel patch is utilized to reduce the intensity jumping at the homogeneous area. The proposed denoising algorithm was compared with related state-of-the-art methods and produced very competitive results over synthetic and real 3D MR data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 34, April 2017, Pages 183-194
نویسندگان
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