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The inherent noise in the observed (e.g., scanned) binary document imagedegrades the image quality and harms the compression ratio through breaking thepattern repentance and adding entropy to the document images. In this paper, wedesign a cost function in Bayesian framework with dictionary learning.Minimizing our cost function produces a restored image which has better qualitythan that of the observed noisy image, and a dictionary for representing andencoding the image. After the restoration, we use this dictionary (from thesame cost function) to encode the restored image following thesymbol-dictionary framework by JBIG2 standard with the lossless mode.Experimental results with a variety of document images demonstrate that ourmethod improves the image quality compared with the observed image, andsimultaneously improves the compression ratio. For the test images withsynthetic noise, our method reduces the number of flipped pixels by 48.2% andimproves the compression ratio by 36.36% as compared with the best encodingmethods. For the test images with real noise, our method visually improves theimage quality, and outperforms the cutting-edge method by 28.27% in terms ofthe compression ratio.

Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning