Integrating guided filter into fuzzy clustering for noisy image segmentation

Abstract

Fuzzy clustering is a classical method to produce soft partitions of data. One of its typical applications is image segmentation. Guided filter, on the other hand, is a powerful edge preserving filter for image smoothing and enhancement. In this work, we design a general framework to improve the fuzzy clustering based noisy image segmentation by integrating the guided filter in a new way. Specifically, the fuzzy clustering is applied on the smoothed image to obtain more homogeneous segments, but the original noisy image is used as the guide of guided filter to post-process the fuzzy memberships in the iteration of clustering. By doing this, the information loss caused by beforehand image smoothing is remedied by the guidance of original noisy image that pulls back subtle details on the boundaries of partitions. In addition, we prove that the memberships post-processed by guided filter still retain the property usually required by fuzzy clustering: for each data point, the sum of its memberships is one. This property and the linear time complexity of guided filter make the proposed information integration framework an efficient way to enhance almost all fuzzy clustering based image segmentation methods. Experiments on synthetic and real images demonstrate that the proposed framework can improve the state-of-the-art fuzzy clustering methods significantly with little run-time overhead.