Fourth Order Variational Multiplicative Noise Removal Model

Abstract - Multiplicative noise removal based on total variation (TV) regularization has been widely researched in image science. In multiplicative noise problems, original image is multiplied by a noise rather than added to the original image. Usually, the logarithmic amplification transforms the multiplicative noise model into the classical additive noise problem. This additive noise problem is then solved by ROF [23] model. In this paper we propose a model for multiplicative noise based on Euler’s Elastica model for additive noise [8]. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details while the noise in the homogeneous regions is removed sufficiently. As a consequence, our method yields better denoised results than those of the current state of the art methods with respect to the SNR values.