A total variable-order variation model for image denoising

In this paper, we explore a new variational model based on the fractional derivative and total variation. Due to some metrics, our approach shows great results compared to other competitive models. In particular, deleting the noise and preserving edges, features and corners are headlights to our approach. For the fractional variable-order derivatives, different discretizations were presented to comparison. The theoretical results are validated by the Primal Dual Projected Gradient (PDPG) Algorithm which is well adapted to the fractional calculus.