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Block-based compressed sensing of MR images using multi-rate deep learning approach
2021
Complex & Intelligent Systems
AbstractDeep learning (DL) models are highly research-oriented field in image compressive sensing in the recent studies. In compressive sensing theory, a signal is efficiently reconstructed from very small and limited number of measurements. Block-based compressive sensing is most promising and lenient compressive sensing (CS) approach mostly used to process large-sized videos and images: exploit low computational complexity and requires less memory. In block-based compressive sensing, a number
doi:10.1007/s40747-021-00426-6
fatcat:fe7xvi2hwfaiph26tx4qltjifa