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To reduce patient's dose, few-view CT reconstruction promises to be a good attempt. The key to better reconstruction is the sparse view artifacts. In recent years, DL(deep learing) has attracted a lot of attention because its outstanding performance in image processing. We propose a deep learning method for few-view CT reconstuction. Our method directly learns an end-to-end mapping between the full-view/few-view...
CT image reconstruction from incomplete projection data is a challenging problem. Among massive reconstruction methods, iterative reconstruction based on compressed sensing (CS) is a promising one that enables us to accurately recovery signals from highly under-sample data when the signals have a sparse representation, which usually can be done by the constrained l1 minimization. The total variation...
Internet of Things (IoT) is coming into birth as pervasive hand-held devices, Radio Frequency IDentification (RFID), and embedded sensors connect to the Internet. IoT enables objects to interact and cooperate with each other anytime and anywhere. Owing to the new challenges imposed by IoT — search locality and real-time, searching in IoT is challenging. To explore what way is appropriate to satisfy...
In this paper, we study the problem of discovering multiple resource holders and how to evaluate a node's satisfaction in query incentive networks. Utilizing an acyclic tree, we show that query propagation has a nature of exponential start, polynomial growth, and eventually becoming a constant. We model the query propagation as an extensive game, obtain nodes' greedy behaviors from Nash equilibrium...
The CT limited-angle problem has been discussed for many years because of the strong demand in medical imaging applications. However as a severely ill-posed problem, it is hard to achieve accurate reconstruction if no additional prior information is given. Recently, an ART+TV reconstruction method was proposed. With the prior knowledge of image sparsity, the ART+TV gives high quality reconstructions...
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