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This article presents a novel neural network-based approach for enhancement of 3D medical image data. The proposed networks learn a sparse representation basis by mapping the corrupted input data to corresponding optimal targets. To reinforce the adjustment of the network to the given data, the threshold values are also adaptively learned. In order to capture important image features on various scales...
While initial compressed sensing (CS) recovery techniques operated under the implicit assumption that the sparse domain coefficients are independently distributed, recent results have indicated that integrating a statistical or structural dependence model of sparse domain coefficients into CS enhances recovery. In this paper, we present a method for exploiting empirical dependences among wavelet coefficients...
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