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Compressed sensing MR image reconstruction based on nonlocal total variation and partially known support. (Chinese. English summary) Zbl 1363.92028
Summary: By exploiting the similarity of structure between the reference and the target images, a novel compressed sensing (CS)-based reconstruction method is proposed for MR image. Indexes of the $$L$$ largest wavelet coefficients of the reference image are extracted and regarded as the known part of the desired target image’s support, and the $$l_1$$ norm of the wavelet coefficients belonging to the complement to the known support is constrained. Furthermore, the nonlocal total variation (NLTV) is utilized as a regularization term to construct the objective function. Then the target image is reconstructed via a fast composite splitting algorithm (FCSA). Experimental results demonstrate that the proposed method can preserve edges and details while suppressing noise efficiently. It outperforms conventional CS-MRI and other similar reconstruction methods under the same sampling rate.
##### MSC:
 92C55 Biomedical imaging and signal processing 94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
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