Data driven regularization by projection
arXiv:1909.11570 · doi:10.1088/1361-6420/abb61b
Abstract
We study linear inverse problems under the premise that the forward operator is not at hand but given indirectly through some input-output training pairs. We demonstrate that regularization by projection and variational regularization can be formulated by using the training data only and without making use of the forward operator. We study convergence and stability of the regularized solutions in view of T. I. Seidman. "Nonconvergence Results for the Application of Least-Squares Estimation to Ill-Posed Problems". Journal of Optimization Theory and Applications 30.4 (1980), pp. 535-547, who showed that regularization by projection is not convergent in general, by giving some insight on the generality of Seidman's nonconvergence example. Moreover, we show, analytically and numerically, that regularization by projection is indeed capable of learning linear operators, such as the Radon transform.
References in corpus (8)
- Deep Convolutional Neural Network for Inverse Problems in Imaging
- On instabilities of deep learning in image reconstruction - Does AI come at a cost?
- Solving ill-posed inverse problems using iterative deep neural networks
- Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
- Deep Null Space Learning for Inverse Problems: Convergence Analysis and Rates
- Adversarial Regularizers in Inverse Problems
- A data-driven iteratively regularized Landweber iteration
- Discretization of variational regularization in Banach spaces
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