PRFCM: Poisson-Specific Residual-Driven Fuzzy C-Means Clustering for Image Segmentation

A Fuzzy C-Means (FCM) algorithm has been widely applied to image segmentation due to its simplicity and effectiveness. However, conventional FCM and its variants often struggle to maintain robustness and accuracy when dealing with complex noise environments, particularly Poisson and mixed Poisson-Gaussian noise. To address this shortcoming, this work proposes a novel Poisson-specific Residual-driven FCM (PRFCM) algorithm for robust image segmentation, which is the first work to develop a dedicated residual regularization mechanism that effectively realizes the robust estimation of Poisson noise (regarded as residual between noisy and noise-free images). It incorporates a weighted ℓ₂-norm regularization term with respect to Poisson noise distribution into FCM. An iterative residual approximation method is introduced to solve the minimization problem about residual, thus simplifying PRFCM's optimization procedure and enhancing its computational efficiency. PRFCM is also extended to cope with mixed Poisson-Gaussian noise scenarios without compromising performance. Experimental results on both simulated and real-scene images demonstrate that the proposed approach outperforms other FCM-related methods in terms of segmentation accuracy, noise resilience, and structural preservation, especially in challenging noise conditions.