A novel blind restoration and reconstruction approach for CT images based on sparse representation and hierarchical Bayesian-MAP

DOI10.3390/a12080174zbMath1451.94015OpenAlexW2967003917WikidataQ127354662 ScholiaQ127354662MaRDI QIDQ2005580

Liyi Zhang, Yunshan Sun, Juan Meng, Yanqin Li

Publication date: 8 October 2020

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a12080174

zbMATH Keywords

compressed sensingCT reconstructionblind restorationhierarchical Bayesian-MAP

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Adapting total generalized variation for blind image restoration
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• Near-Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices Via Sparse Power Factorization
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• X-ray Computed Tomography using a Sparsity Enforcing Prior Model Based on Haar Transformation in a Bayesian Framework
• Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on Generalized Gaussian Priors
• Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain
• Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures

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