Image feature enhancement based on the time-controlled total variation flow formulation

Data smoothing and feature enhancement are two important precursors to many higher-level computer vision applications such as image segmentation and scene understanding. Total variation (TV) flow algorithms are a distinct subcategory of diffusion-based filtering techniques that have been widely applied to reduce the level of noise in the image but not at the expense of poor feature preservation. In this paper we address a number of numerical aspects associated with the TV flow and in particular we are interested to redefine the TV flow regularization in order to reduce the effect of oscillations and improve the convergence of the implementations in the discrete domain. TV flow algorithms are implemented using iterative schemes and one difficult problem is the selection of appropriate criteria to identify the optimal number of iterations. In this paper we show that the application of a time-ageing procedure leads to an elegant formulation were the TV flow algorithms converge naturally to the optimal solution. To evaluate the performance of the proposed algorithm (referred in this paper to as time-controlled (TC)-TV flow), a large number of experiments on various types of natural images were conducted.