Treffer: A truncated generalized Huber prior for image smoothing.

• We propose a nonconvex prior for image smoothing. • The proposed algorithm of the nonconvex model has a convergence guarantee. • The performance of the prior outperforms the state-of-the-art. • The proposed method is flexible, convergent and has promising results. Image smoothing is a fundamental task in computer vision and graphics. This paper presents a new image smoothing method based on a truncated generalized Huber prior. The proposed model is neither convex nor concave and is hard to optimize. We first transform the prior into a concave one, then utilize the technique of half-quadratic minimization to get an equivalent convex surrogate function. Thus the numerical algorithm is obtained by solving a weighted least square problem and iteratively updating the weights. The convergence of the algorithm is theoretically guaranteed. The proposed method is flexible and powerful in preserving edge/structure and eliminating undesired information. The effectiveness of the proposed method is demonstrated by several applications, including scale space filtering, texture removal, and clip-art JPEG artifacts removal. [ABSTRACT FROM AUTHOR]