Semantic image content filtering via edge-preserving scale-aware filter

In this paper, we highlight a new filtering concept and methodology, called the semantic image content filtering (SICF), which aims to remove insignificant small details from the image while preserving its main structure. Such image content separation is not possible to achieve by using any conventional linear filter as it is essentially designed to perform frequency separation. To realize an effective SICF, a novel image filtering algorithm, called the edge-preserving scale-aware filter (ESF), is proposed in this paper. Our proposed ESF yields a significant improvement over a recently-developed scale-aware filter, called the rolling guidance filter (RGF). The key success of our ESF lies in the developed adaptive relative total variation filter (ARTVF), which replaces the RGF's Gaussian filter for generating a much improved initial guidance image. Extensive simulation results obtained from various test images have clearly demonstrated that the proposed ESF outperforms other state-of-the-art methods on conducting SICF task. That is, the semantically-important large-scale image structure has been better preserved, while the insignificant small details have been removed more effectively.