Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise

In this paper, we present the Block-Matching Fuzzy C-Means (BMFCM) clustering algorithm to segment RGB color images degraded with Additive White Gaussian Noise (AWGN). The contribution of this paper is threefold, namely, noise level estimation, denoising and segmentation. First, two Additive White Gaussian Noise estimation algorithms are proposed to compute the noise variance of the observed noisy color image. Second, we propose an image denoising method based on the enhanced sparse representation using a Block-Matching approach. Third, the Block-Matching Fuzzy C-Means clustering algorithm is proposed. The motivation behind the proposed clustering algorithm is to improve the characteristics of the standard Fuzzy C-Means algorithm, and apply them to segment noisy color images. For this reason, the local information of every color component is incorporated in the Fuzzy C-Means using the proposed Block-Matching based filter as an Additive White Gaussian Noise estimator to determine whether the central pixel in a sliding window is noisy. The presented Additive White Gaussian Noise estimation algorithms are used in the proposed Block-matching method to improve its accuracy. The chromatic subspace of the IJK color space is also applied in the proposed clustering approach providing better segmentation results and reducing the processing time; this is because the algorithm is reduced in a bi-dimensional clustering approach. Finally, visual and numerical experiments demonstrate that the proposed algorithms provide better segmentation results in the presence and absence of AWGN in comparison with other segmentation methods.