Papers › Total variation regularization with variable Lebesgue prior
Total variation regularization with variable Lebesgue prior
Holger Kohr
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
This work proposes the variable exponent Lebesgue modular as a replacement for the 1-norm in total variation (TV) regularization. It allows the exponent to vary with spatial location and thus enables users to locally select whether to preserve edges or smooth intensity variations. In contrast to earlier work using TV-like methods with variable exponents, the exponent function is here computed offline as a fixed parameter of the final optimization problem, resulting in a convex goal functional. The obtained formulas for the convex conjugate and the proximal operators are simple in structure and can be evaluated very efficiently, an important property for practical usability. Numerical results with variable Lᵖ TV prior in denoising and tomography problems on synthetic data compare favorably to total generalized variation (TGV) and TV.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections