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2516. Space-Variant Total Variation boosted by learning techniques for subsampled imaging problems
Invited abstract in session MC-34: Optimization and learning for data science and imaging (Part III), stream Advances in large scale nonlinear optimization.
Monday, 12:30-14:00Room: 43 (building: 303A)
Authors (first author is the speaker)
| 1. | Davide Evangelista
|
| University of Bologna |
Abstract
In solving inverse problems with subsampled forward operators, a crucial aspect is choosing an efficient regularizer. The isotropic Total Variation (TV) functional is often used uniformly, to get gradient-sparse solutions, but it tends to remove small details and smooth edges. To address this, we propose a space-variant weighted isotropic TV regularization, whose weights can be determined by a neural network. This talk includes a rigorous theoretical analysis and presents experiments confirming the potential of the proposed approach to enhance the quality of undersampled CT reconstructions.
Keywords
- Mathematical Programming
- Machine Learning
- Algorithms
Status: accepted
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