Automated removal of quasiperiodic noise using frequency domain statistics

Frédéric Sur 1 Michel Grediac 2
1 MAGRIT - Visual Augmentation of Complex Environments
Inria Nancy - Grand Est, LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
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.
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Dernière modification le : mardi 18 décembre 2018 - 16:18:26
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Frédéric Sur, Michel Grediac. Automated removal of quasiperiodic noise using frequency domain statistics. Journal of Electronic Imaging, SPIE and IS&T, 2015, 24 (1), pp.013003/1-19. 〈〉. 〈10.1117/1.JEI.24.1.013003〉. 〈hal-01116309〉



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