ArticleIEEE transactions on computational imaging2024
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction.
Article in IEEE transactions on computational imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers.IEEE transactions on computational imaging · 2026Article
- Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction.IEEE transactions on computational imaging · 2025Article
- Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction.IEEE transactions on computational imaging · 2025Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
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4 authors.
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Abstract
Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a general framework that uses denoising algorithms as the prior or regularizer. Recent work showed that PnP methods with denoisers based on pretrained convolutional neural networks outperform other classical regularizers in CS MRI reconstruction. However, the numerical solvers for PnP can be slow for CS MRI reconstruction. This paper proposes a preconditioned PnP
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