A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
[article]
2017
arXiv
pre-print
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 the
arXiv:1704.07019v1
fatcat:yx6vqschuvguvoo46tab32v7ii