Image denoising based on K-Means clustering and binary tree decision
Yongxia Liu · Computer Engineering and Science · 2013
In this paper , a new filter algorithm was proposed for pepper-and-salt noise suppression. Firstly , the neighborhood of each given pixel is partitioned by K-means clustering according to the local grey level distribution.Secondly , the recognition rules for noise-polluted pixel detection are constructed , and the noise pixel can be detected based on multi-layer binary tree decision.The proposed algorithm only filters the recognized noise pixels without changing those non-polluted pixel values.Experimental results show that the proposed algorithm can efficiently preserve informative details when filtering image noise.For those images with strong noise pollution , the proposed algorithm outperforms both median filter and the algorithm proposed in [7] .