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Automated removal of quasiperiodic noise using frequency domain statistics

[+] Author Affiliations
Frédéric Sur

Université de Lorraine, LORIA UMR CNRS 7503; CNRS, INRIA project-team Magrit; Campus Scientifique, BP 239, Vandœuvre-lès-Nancy Cedex 54506, France

Michel Grédiac

Université Blaise Pascal, Institut Pascal UMR CNRS 6602, CNRS, BP 10448, Clermont-Ferrand 63000, France

J. Electron. Imaging. 24(1), 013003 (Feb 11, 2015). doi:10.1117/1.JEI.24.1.013003
History: Received June 11, 2014; Accepted January 7, 2015
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Abstract.  Digital images may be impaired by periodic or quasiperiodic noise, which manifests itself by spurious long-range repetitive patterns. Most of the time, quasiperiodic noise is well localized in the Fourier domain; thus it can be attenuated by smoothing out the image spectrum with a well-designed notch filter. While existing algorithms require hand-tuned filter design or parameter setting, this paper presents an automated approach based on the expected power spectrum of a natural image. The resulting algorithm enables not only the elimination of simple periodic noise whose influence on the image spectrum is limited to a few Fourier coefficients, but also of quasiperiodic structured noise with a much more complex contribution to the spectrum. Various examples illustrate the efficiency of the proposed algorithm. A comparison with morphological component analysis, a blind source separation algorithm, is also provided. A MATLAB® implementation is available.

© 2015 SPIE and IS&T

Citation

Frédéric Sur and Michel Grédiac
"Automated removal of quasiperiodic noise using frequency domain statistics", J. Electron. Imaging. 24(1), 013003 (Feb 11, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013003


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