Adaptive weighted guided image filtering for image denoising based on artificial swarm optimization

To solve the shortcomings of traditional guided image filtering (GIF) in edge preservation and denoising performance, this study describes a novel generalized guided image filtering method, which integrates an artificial swarm optimization algorithm. A locally adaptive weighting based on monogenic phase congruency and chaotic swarm optimization is used to produce a more robust method. Since the fixed regularization parameter cannot adapt to the grayscale difference between flat and edge patches, the box filter radius and regularization parameter of guided image filtering have significant influences on image-denoising effects. The chaotic swarm optimization algorithm, which is an improved optimization algorithm with a self-adapting search space, is adopted to find their optimal values for the best denoising effects. Compared with traditional guided image filtering for image denoising and other state-of-the-art methods with image quality as a performance metric, experimental results showed that the proposed denoising algorithm can not only remove noise efficiently and reduce halo artifacts, but can also preserve the edge texture well.