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• W2949810417 abstract "There has been a growing interest in the use of data-driven regularizers to solve inverse problems associated with computational imaging systems. The convolutional sparse representation model has recently gained attention, driven by the development of fast algorithms for solving the dictionary learning and sparse coding problems for sufficiently large images and data sets. Nevertheless, this model has seen very limited application to tomographic reconstruction problems. In this paper, we present a model-based tomographic reconstruction algorithm using a learnt convolutional dictionary as a regularizer. The key contribution is the use of a data-dependent weighting scheme for the l1 regularization to construct an effective denoising method that is integrated into the inversion using the Plug-and-Play reconstruction framework. Using simulated data sets we demonstrate that our approach can improve performance over traditional regularizers based on a Markov random field model and a patch-based sparse representation model for sparse and limited-view tomographic data sets." @default.
• W2949810417 created "2019-06-27" @default.
• W2949810417 creator A5015244709 @default.
• W2949810417 creator A5077586593 @default.
• W2949810417 date "2019-05-01" @default.
• W2949810417 modified "2023-09-26" @default.
• W2949810417 title "Convolutional Dictionary Regularizers for Tomographic Inversion" @default.
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• W2949810417 doi "https://doi.org/10.1109/icassp.2019.8682637" @default.
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