Higher order variational multiplicative noise removal model

Multiplicative noise based on Total Variation (TV) regularization has been widely researched in many image processing applications, such as Synthetic Aperture Radar (SAR) images, Ultrasound imaging, single particle emission-computed tomography etc. In such problems, the noise is multiplied to the original image rather than added to the original image. Usually the logarithmic amplification is used to transfer the multiplicative noise to classical additive noise problem. Then this additive noise problem is solved by ROF model. In this paper we develop a new model for multiplicative noise with a modified regularization term based on Euler´s Elastica and Curvatre Based Inpainting model Our experimental results show that the new model has a good performance than the current state of art method with respect to the SNR values.