LAUSR

The main goals of denoising are to improve the signal-to-noise-ratio (SNR) and to preserve the informative features such as edges and textures. Aiming at reducing Gibbs-type artifacts, several researchers have combined wavelet-like transforms such as curvelets with total variation or diffusion methods. In this paper, a ripplet formulation of the total variation method for denoising images is proposed. The ripplet is known as a developed version of the curvelet transform and proposes a new tight frame with sparse representation for images with discontinuities along any type of boundaries. We manipulate the cost function of the total variation method, such that instead of minimizing the total variation of the noisy image, we minimize the total variation of a new image obtained from non-textured regions of ripplet subbands. To obtain these regions, ripplet coefficients are divided into textured regions and smooth ones using the twin support vector machine classifier. Numerical examples demonstrate that the proposed approach improves the image quality in terms of both subjective and objective inspections, compared with some other state-of-the-art denoising techniques.