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The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the observed noisy image, and a dictionary for representing and encoding the image. After thedoi:10.1109/cvpr.2017.72 dblp:conf/cvpr/GuoLAB17 fatcat:kmobkxlwhzcwljvy4wxsvuxg6a