Hyperbolic tangent penalty function for edge-preserving image filtering

Abstract

Edge-preserving image filtering provides a common interface for a variety of edge-aware image processing tasks. It is essential to the field of image processing and computational photography. Recent attempts at optimization models with non-convex regularization have shown promising performance in edge-preserving image filtering. In this paper, we propose a novel non-convex regularization term based on a hyperbolic tangent penalty function, which is shown to be more edge-preserving than popular non-convex penalty functions. We embed the proposed regularization term in an optimization model for edge-preserving image filtering. To solve the model with the non-convex regularization, we propose an efficient solution based on the additive half quadratic minimization and Fourier domain optimization. We have conducted extensive experiments to evaluate the proposed filter. Both quantitative and qualitative results demonstrate that our filter benefits various applications. Furthermore, the proposed filter is highly efficient and renders real-time processing of 720P color images on a modern GPU.