Reconstruction-Aware Kernelized Fuzzy Clustering Framework Incorporating Local Information for Image Segmentation

Abstract

Kernelized fuzzy C-means clustering with weighted local information is an extensively applied robust segmentation algorithm for noisy image. However, it is difficult to effectively solve the problem of segmenting image polluted by strong noise. To address this issue, a reconstruction-aware kernel fuzzy C-mean clustering with rich local information is proposed in this paper. Firstly, the optimization modeling of guided bilateral filtering is given for noisy image; Secondly, this filtering model is embedded into kernelized fuzzy C-means clustering with local information, and a novel reconstruction-filtering information driven fuzzy clustering model for noise-corrupted image segmentation is presented; Finally, a tri-level alternative and iterative algorithm is derived from optimizing model using optimization theory and its convergence is strictly analyzed. Many Experimental results on noisy synthetic images and actual images indicate that compared with the latest advanced fuzzy clustering-related algorithms, the algorithm presented in this paper has better segmentation performance and stronger robustness to noise, and its PSNR and ACC values increase by about 0.16–3.28 and 0.01–0.08 respectively.