Feature-preserving filtering with L0 gradient minimization

Abstract

Feature-preserving filtering is a fundamental tool in computer vision and graphics, which can smooth input signal while preserving its sharp features. Recently, a piecewise smooth model called L0 gradient minimization, has been proposed for feature-preserving filtering. Through optimizing an energy function involving gradient sparsity prior, L0 gradient minimization model has strong ability to keep sharp features. Meanwhile, due to the non-convex property of L0 term, it is a challenge to solve the L0 gradient minimization problem. The main contribution of this paper is a novel and efficient approximation algorithm for it. The energy function is optimized in a fused coordinate descent framework, where only one variable is optimized at a time, and the neighboring variables are fused together once their values are equal. We apply the L0 gradient minimization in two applications: (i) edge-preserving image smoothing (ii) feature-preserving surface smoothing, and demonstrate its good performance.