Predictor-Corrector Method for Total Variation Based Image Denoising

Abstract

Since \u00a0their \u00a0introduction \u00a0in \u00a0a \u00a0classic \u00a0paper \u00a0by \u00a0Rudin, \u00a0Osher \u00a0and \u00a0Fetemi \u00a0[1], \u00a0total \u00a0variation minimizing models have become one of the most popular and successful methodology for image restoration. More recently, there has been a resurgence of interest and exciting new developments, some extending the applicabilities to inpainting, blind deconvolution and vector-valued images, while others offer improvements in \u00a0better \u00a0preservation \u00a0of \u00a0contrast, \u00a0geometry \u00a0and \u00a0textures, \u00a0in \u00a0ameliorating \u00a0the \u00a0staircasing \u00a0effect, \u00a0and \u00a0in exploiting the multiscale nature of the models. In addition, new computational methods have been proposed with \u00a0improved \u00a0computational \u00a0speed \u00a0and \u00a0robustness. \u00a0In \u00a0this \u00a0paper, \u00a0a \u00a0predictor-Corrector \u00a0techniques \u00a0are pointed out and applied in to the total variation-based image denoising.The numerical experiments shows the improvement are fairly valid.