Estimating degradation model parameters using neighborhood pattern distributions: an optimization approach
Kanungo, T.
Qigong Zheng
IBM Almanden Res. Center, San Jose, CA, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: April 2004
Volume: 26,
Issue: 4
On page(s): 520-524
ISSN: 0162-8828
INSPEC Accession Number: 7958012
Digital Object Identifier: 10.1109/TPAMI.2004.1265867
Current Version Published: 2004-02-19
Abstract
Noise models are crucial for designing image restoration algorithms, generating synthetic training data, and predicting algorithm performance. There are two related but distinct estimation scenarios. The first is model calibration, where it is assumed that the input ideal bitmap and the output of the degradation process are both known. The second is the general estimation problem, where only the image from the output of the degradation process is given. While researchers have addressed the problem of calibration of models, issues with the general estimation problems have not been addressed in the literature. In this paper, we describe a parameter estimation algorithm for a morphological, binary, page-level image degradation model. The inputs to the estimation algorithm are 1) the degraded image and 2) information regarding the font type (italic, bold, serif, sans serif). We simulate degraded images using our model and search for the optimal parameter by looking for a parameter value for which the local neighborhood pattern distributions in the simulated image and the given degraded image are most similar. The parameter space is searched using a direct search optimization algorithm. We use the p-value of the Kolmogorov-Smirnov test as the measure of similarity between the two neighborhood pattern distributions. We show results of our algorithm on degraded document images.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
