Scale-Aware Edge-Preserving Image Filtering via Iterative Global Optimization
Presently, few filters are able to smooth images in a scale-aware manner like Gaussian filtering while not blurring the edges of large-scale features, whereas this kind of filter can be important in many visual applications requiring scale-aware manipulation while avoiding halos. In this paper, we propose a filtering technique through iterative global optimization (IGO), enabling to achieve both good scale-aware and edge-preserving performance. Our method is based on a filtering idea of selective gradient suppression and guidance gradient correction in the framework of IGO, which has the advantages of avoiding halos and preventing oversharpening of edges, and a scale-aware measure can be introduced to further control the way of gradient suppression. The proposed measure is spatially varying and oriented by coarse-scale local extrema at each pixel to better preserve the natural boundaries of large-scale structures. Besides, we show that our method can be fast implemented with a sequence of 1-D filtering. In the experiments, we demonstrate the effectiveness of our method by comparing it with current state-of-the-art filtering methods and using it in a variety of applications.