I. Sitdikov

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In this paper we propose a new variational image deringing method. This method is based on total variation minimization with an adaptively varying regularization parameter. The proposed approach improves visual quality of resulting images by preserving more structural information comparing to existing methods. Attendant parallel algorithms have been(More)
In this paper, we propose an improved acceleration scheme for the mutual entropy maximization method for biomedical image registration. Our approach is based on fast adaptive bidirectional empirical mode decomposition (FABEMD) and aims to reduce the computational complexity of the mutual entropy maximization algorithm by extracting only information(More)
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