A Cluster-Based Adaptive Switching Median Filter

This paper presents a cluster-based adaptive weight switching median filter. Clustering analysis and a linear function is combined to capture local image statistics. In term of the local information, an iteration function is constructed to subtract impulses from corrupted image and thus noise detector is defined. After the noisy pixels are identified, in order to keep image details as intact as possible, a cluster-based adaptive weighted median filter is proposed to estimate those noise candidates´ values. Simulation results show that the proposed method provides better performance in term of PSNR and MAE than many existing random-valued impulse noise filtering techniques.