Salt and pepper noise removal in binary images using image block prior probabilities

• The maximum a posteriori probability estimation of each image block is applied.
• Training images are used to compute the prior probability for each block.
• The noise level is estimated automatically.
• The output image PSNR is significantly higher compared with the state of the art.

During scanning and transmission, images can be corrupted by salt and pepper noise, which negatively affects the quality of subsequent graphic vectorization or text recognition. In this paper, we present a new algorithm for salt and pepper noise suppression in binary images. The algorithm consists of the computation of block prior probabilities from training noise-free images; noise level estimation; and the maximum a posteriori probability estimation of each image block. Our experiments show that the proposed method performs significantly better than the state of the art techniques.