Actor-Critic Bilateral Filter for Noise-Robust Image Smoothing

Bilateral filters have been used for achieving excellent edge-preserving image smoothing. However, most studies have focused on the acceleration of bilateral filtering but not on the stability of filtering process in regard to small perturbations to its inputs. In this paper, we propose a novel actor–critic bilateral filter trained with a multistep learning scheme for high-stability edge-preserving image smoothing. We first designed an edge-preserving smoothing process as a Markov decision process that involves adjusting the width setting for the range kernel of a bilateral filter. Next, we trained our actor–critic bilateral filter in a multistep manner to learn the optimal sequence of width settings. Through extensive experiments on five benchmark datasets, we determined that the proposed actor–critic bilateral filter produced satisfactory edge-preserving smoothing results.