A new method is developed for assessing the image edge width based on the unsharp masking approach. A model of the image edge is proposed and a complete solution of the edge width determination problem is obtained. The accuracy of edge determination is analyzed as a function of the length of segments on which profile information is specified and the noise level. An application of the method to assess the quality of an ophthalmological image is reported.
Similar content being viewed by others
References
M. Goncalves and F. Ernst, “Single-image motion and camera blur identification,” Technical Report PR-TN-2005/00298, Philips Research, Eindhoven (2005).
M. Basu, “Gaussian-based edge-detection methods — a survey,” IEEE Transactions on Systems, Man and Cybernetics, Part C, 32, No. 3, 252 − 260 (2002).
K. Suzuki, I. Horiba, and N. Sugie, “Neural edge enhancer for supervised edge enhancement from noisy images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, No. 12, 1582 − 1596 (2003).
H. Hu and G. de Haan, “Low Cost Robust Blur Estimator,” IEEE International Conference on Image Processing, 617 − 620 (2006).
E. Nadernejad, “Edge detection techniques: evaluations and comparisons,” Appl. Math. Sci., 2, 1507–1520 (2008).
S. Krylov and M. Najafi, “A projection method for edge detection in images,” J. Comput. Math. Model., 18, 91–101 (2007).
A. Chernomorets and A. V. Nasonov, “Deblurring in fundus images,” Proc. Int. Conf. Comp. Graph. GraphiCon’2012. Moscow (2012), pp. 76 − 79.
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated from Prikladnaya Matematika i Informatika, No. 42, 2013, pp. 76–82.
Rights and permissions
About this article
Cite this article
Nasonova, A.A., Krylov, A.S. Determination of Image Edge Width by Unsharp Masking. Comput Math Model 25, 72–78 (2014). https://doi.org/10.1007/s10598-013-9208-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10598-013-9208-8