Total variation regularization with variable Lebesgue prior

Abstract: 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 $L^p$ TV prior in denoising and tomography problems on synthetic data compare favorably to total generalized variation (TGV) and TV.