A COMBINED FIRST-ORDER AND SECOND-ORDER VARIATION APPROACH FOR MULTIPLICATIVE NOISE REMOVAL. (October 2014) - JIANG, LE; HUANG, JIN; LIU, JUN; LV, XIAO-GUANG
Abstract Denoising of images corrupted by multiplicative noise is an important task in various applications, such as laser imaging, synthetic aperture radar and ultrasound imaging. We propose a combined first-order and second-order variational model for removal of multiplicative noise. Our model substantially reduces the staircase effects while preserving edges in the restored images, since it combines advantages of the first-order and second-order total variation. The issues of existence and uniqueness of a minimizer for this variational model are analysed. Moreover, a gradient descent method is employed to solve the associated Euler–Lagrange equation, and several numerical experiments are given to show the efficiency of our model. In particular, a comparison with an existing model in terms of peak signal-to-noise ratio and structural similarity index is provided.