Bayesian Networks for Edge Preserving Salt and Pepper Image Denoising

In this paper we propose a two-step filter for removing salt-and-pepper impulse noise. In the first phase, a Naive Bayesian network is used to identify pixels, which are likely to be contaminated by noise (noise candidates). In the second phase, the noisy pixels are restored according to a regularization method (based on the optimization of a convex functional) to apply only to those selected noise candidates. The proposed method shows a significant improvement compared to other non linear filters or regularization methods in terms of image details preservation and noise reduction. Our algorithm is also able to remove salt-and-pepper-noise with high noise levels since 70% until 90%.