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• W2231879079 abstract "This paper proposes a spatial-Radon domain CT image reconstruction model based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model combines the idea of joint image and Radon domain inpainting model of cite{Dong2013X} and that of the data-driven tight frames for image denoising cite{cai2014data}. It is different from existing models in that both CT image and its corresponding high quality projection image are reconstructed simultaneously using sparsity priors by tight frames that are adaptively learned from the data to provide optimal sparse approximations. An alternative minimization algorithm is designed to solve the proposed model which is nonsmooth and nonconvex. Convergence analysis of the algorithm is provided. Numerical experiments showed that the SRD-DDTF model is superior to the model by cite{Dong2013X} especially in recovering some subtle structures in the images." @default.
• W2231879079 created "2016-06-24" @default.
• W2231879079 creator A5035683291 @default.
• W2231879079 creator A5053491985 @default.
• W2231879079 date "2016-01-05" @default.
• W2231879079 modified "2023-10-16" @default.
• W2231879079 title "CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization" @default.
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• W2231879079 doi "https://doi.org/10.48550/arxiv.1601.00811" @default.
• W2231879079 hasPublicationYear "2016" @default.
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