Soft Clustering Guided Image Smoothing

Abstract

Image smoothing, which aims to remove unwanted textures and preserve desired structures, plays an important role in many multimedia and computer vision tasks. The key to image smoothing, despite different applications, is to distinguish the structures from the textures. This paper presents a novel image smoothing method, following the principle that, for a certain pixel, its neighbors in both space and intensity should contribute more on smoothing, while the distant ones be insulated for avoiding over-smoothing. Intuitively, clustering is a good candidate to achieve the goal. However, due to rich textures and clutters within images, simply performing the clustering on the input likely obtains inaccurate results, and thus leads to unsatisfied smoothing results. In addition, for our task, using traditional hard clustering techniques is at high risk of generating staircase artifacts. For addressing these issues, an algorithm is customized, which on the one hand adopts the soft clustering to more faithfully assign pixels, on the other hand iterates the soft clustering and smoothing, expecting to improve each other. Experiments on several challenging images are provided to show the efficacy of our method, and its superiority over other prevailing approaches.