Structure-preserving image smoothing with semantic cues

The purpose of image smoothing is to smooth out low-contrast textures while preserving meaningful structures. Although this problem has been studied for decades, it still leaves a lot of space to improve. Recently, learning-based edge detectors have superior performance to traditional manually-designed detectors. Based on the edge detection technique, we present a novel optimization-based image smoothing model combining semantic prior and perform L0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document} gradient minimization recursively in our framework to refine the result. Our framework combines the advantage of the state-of-the-art edge detector and the ability of L0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_0$$\end{document} gradient minimization for structure-preserving image smoothing. Moreover, we employ a large number of real-world images and perform various experiments to evaluate our algorithm. Experimental results show that our algorithm outperforms state-of-the-art algorithms, especially in extracting subjectively-meaningful structures.