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[2602.21707] Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Summary
This paper presents a novel approach to image reconstruction using spatially adaptive sparsity level maps within convolutional dictionaries, enhancing robustness and interpretability in low-field MRI applications.
Why It Matters
The research addresses critical challenges in image reconstruction methods, particularly their interpretability and robustness. By integrating model-based techniques with neural networks, this work offers a promising solution to improve performance in medical imaging, potentially impacting diagnostic accuracy and patient care.
Key Takeaways
- Introduces spatially adaptive sparsity level maps for image reconstruction.
- Demonstrates improved robustness against data distribution shifts.
- Achieves filter-permutation invariance in convolutional dictionaries.
- Shows effectiveness in low-field MRI applications compared to existing methods.
- Highlights reduced reliance on training data due to model-based components.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.21707 (eess) [Submitted on 25 Feb 2026] Title:Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries Authors:Joshua Schulz, David Schote, Christoph Kolbitsch, Kostas Papafitsoros, Andreas Kofler View a PDF of the paper titled Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries, by Joshua Schulz and 4 other authors View PDF HTML (experimental) Abstract:State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about their interpretability and robustness. Here, we build on a recently proposed image reconstruction method, which is based on embedding data-driven information into a model-based convolutional dictionary regularization via neural network-inferred spatially adaptive sparsity level maps. By means of improved network design and dedicated training strategies, we extend the method to achieve filter-permutation invariance as well as the possibility to change the convolutional dictionary at inference time. We apply our method to low-field MRI and compare it to several other recent deep learning-based methods, also on in vivo data, in which the benefit for the use of a different dictionary is showcased. We further assess the method's robustness when tested on in- and out-of-distribution data. When tested on the latter, the proposed method suffer...