1. Parameter learning and fractional differential operators: Applications in regularized image denoising and decomposition problems
- Author
-
Sören Bartels and Nico Weber
- Subjects
Control and Optimization ,Partial differential equation ,Computer science ,Riesz potential ,Applied Mathematics ,Differential operator ,Bilevel optimization ,Regularization (mathematics) ,Reduction (complexity) ,symbols.namesake ,Fourier transform ,Operator (computer programming) ,Computer Science::Computer Vision and Pattern Recognition ,symbols ,Applied mathematics - Abstract
In this paper, we focus on learning optimal parameters for PDE-based image denoising and decomposition models. First, we learn the regularization parameter and the differential operator for gray-scale image denoising using the fractional Laplacian in combination with a bilevel optimization problem. In our setting the fractional Laplacian allows the use of Fourier transform, which enables the optimization of the denoising operator. We prove stable and explainable results as an advantage in comparison to machine learning approaches. The numerical experiments correlate with our theoretical model settings and show a reduction of computing time in contrast to the Rudin-Osher-Fatemi model. Second, we introduce a new regularized image decomposition model with the fractional Laplacian and the Riesz potential. We provide an explicit formula for the unique solution and the numerical experiments illustrate the efficiency.
- Published
- 2023