Submitted Paper

Inserted: 12 may 2023
Last Updated: 12 may 2023

Year: 2023

Abstract:

Due to their ability to handle discontinuous images while having a well-understood behavior, regularizations with total variation (TV) and total generalized variation (TGV) are some of the best-known methods in image denoising. However, like other variational models including a fidelity term, they crucially depend on the choice of their tuning parameters. A remedy is to choose these automatically through multilevel approaches, for example by optimizing performance on noisy or clean image pairs. In this work, we consider such methods with space-dependent parameters which are piecewise constant on dyadic grids, with the grid itself being part of the minimization. We prove existence of minimizers for fixed discontinuous parameters, that box constraints for the values of the parameters lead to existence of finite optimal partitions, and converse results for well-prepared data. On the numerical side, we consider a simple subdivision scheme for optimal partitions built on top of any other bilevel optimization method for scalar parameters, and demonstrate its improved performance on some representative test images when compared with constant optimized parameters.

Keywords: Total variation, total generalized variation, discontinuous weights, spatially-dependent regularization parameters, box constraint, bilevel optimization

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• Dyadic_image_regularization.pdf