Analytical aspects of spatially adapted total variation regularisation
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Abstract (arXiv)
In this paper we study the structure of solutions of the one dimensional weighted total variation regularisation problem, motivated by its application in signal recovery tasks. We study in depth the relationship between the weight function and the creation of new discontinuities in the solution. A partial semigroup property relating the weight function and the solution is shown and analytic solutions for simply data functions are computed. We prove that the weighted total variation minimisation problem is well-posed even in the case of vanishing weight function, despite the lack of coercivity. This is based on the fact that the total variation of the solution is bounded by the total variation of the data, a result that it also shown here. Finally the relationship to the corresponding weighted fidelity problem is explored, showing that the two problems can produce completely different solutions even for very simple data functions.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
5 papers.
- 2025DEALing with Image Reconstruction: Deep Attentive Least Squarescited by 6
- 2024Stability of Data-Dependent Ridge-Regularization for Inverse Problemscited by 5
- 2024Iteratively Refined Image Reconstruction with Learned Attentive Regularizerscited by 3
- 2021Bilevel Imaging Learning Problems as Mathematical Programs with Complementarity Constraints: Reformulation and Theorycited by 0
- 2026Learning spatially varying regularisation parameters of low regularity for image reconstructioncited by 0
- 2026Learning spatially varying regularisation parameters of low regularity for image reconstruction25 Aug 2026
- 2025DEALing with Image Reconstruction: Deep Attentive Least Squares6 Feb 2025
- 2024Iteratively Refined Image Reconstruction with Learned Attentive Regularizers9 Jul 2024
- 2024Stability of Data-Dependent Ridge-Regularization for Inverse Problems18 Jun 2024
- 2021Bilevel Imaging Learning Problems as Mathematical Programs with Complementarity Constraints: Reformulation and Theory5 Oct 2021