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Calibrating Image Roughness by Estimating Lipschitz Exponents, with Applications to Image Restoration

Published

Author(s)

Alfred S. Carasso

Abstract

Most commonly occurring images f(x,y) are not smoothly differentiable functions of the variables x and y. Rather, these images display edges, localized sharp features, and other significant fine scale details or texture. Correct characterization and calibration of the lack of smoothness in such images is important in various image processing tasks. So-called Lipschitz spaces appear to be the appropriate mathematical framework for accommodating non smooth images. The L1 Lipschitz exponent ?? for the given image, where ~0
Citation
Optical Engineering
Volume
47
Issue
3

Keywords

Andromeda galaxy, APEX method, Gauss singular integral, image metrology, image roughness, Lipschitz exponents

Citation

Carasso, A. (2008), Calibrating Image Roughness by Estimating Lipschitz Exponents, with Applications to Image Restoration, Optical Engineering, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=51213 (Accessed April 19, 2024)
Created March 3, 2008, Updated January 27, 2020