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In this paper, we propose a novel compressed sensing (CS) algorithm by collaborative use of statistical and structural priors of natural images. The statistical priors include two aspects which are the statistical dependencies of wavelet coefficients in transform domain and non-local self- similarity among pixels in spatial domain. And the structural prior refers to the structural dependencies of...
Compressed sensing (CS) based image processing is a important branch of CS based signal processing. However, the high complexity involved in CS based large-size image processing is a challenging issue. In this paper, an orthogonal matching pursuit enabled two-stage CS recovery scheme with low complexity yet high performance, termed separate-combine recovery method is proposed for separable image sensing...
Diffusion tensor imaging is a special magnetic resonance imaging method and is widely used to characterize tissue micro architecture and brain connectivity. However, DTI requires long acquisition time due to the repetitive examination with different diffusion gradients and is easily suffered from motion artifacts. These drawbacks greatly limit the clinical application of DTI. In this study, we proposed...
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