Spatial image filtering using spline approximation in the case of impulse noise

D. Bezuglov; V. Voronin; S. Mishchenko

doi:10.1117/12.2519681

13 May 2019 Spatial image filtering using spline approximation in the case of impulse noise

D. Bezuglov, V. Voronin, S. Mishchenko

Proceedings Volume 10990, Computational Imaging IV; 109900Y (2019) https://doi.org/10.1117/12.2519681
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Recently, systems of real-time intellectual processing of television images have been developing intensively. There are special requirements for modern computational imaging systems for accuracy due to the high variability of the working scene, the heterogeneity of objects, and interference. The process of digital images processing in a priori unknown observation conditions significantly complicates the presence of impulse noise due to various factors, such as defects in the recording system, as well as environmental influences. One of the trends in modern information technologies is the development of highly efficient and computational methods for image filtering. The most filtering methods and algorithms are required a priori knowledge of the characteristics of distorting interference. In practice, in most cases, such information is missing or approximate. Currently, one of the urgent tasks of image processing is the problem of the objects contour detection in the presence of various interference. This paper considers the new real-time algorithm for spatial images filtering using spline approximation in the case of impulse noise. Mathematical modeling of the proposed approaches was carried out, and their advantages were shown. The results of testing the proposed methods on the test database of images in comparison with known methods are presented.

Citation Download Citation

D. Bezuglov, V. Voronin, and S. Mishchenko "Spatial image filtering using spline approximation in the case of impulse noise", Proc. SPIE 10990, Computational Imaging IV, 109900Y (13 May 2019); https://doi.org/10.1117/12.2519681

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