Adaptive comb-type filtering method for stripe noise removal in infrared images

Jihong Pei; Mi Zou; Yang Zhao

doi:10.1117/1.JEI.28.1.013037

18 February 2019 Adaptive comb-type filtering method for stripe noise removal in infrared images

Jihong Pei, Mi Zou, Yang Zhao

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013037 (February 2019). https://doi.org/10.1117/1.JEI.28.1.013037

Abstract

Occasionally, stripe noise infiltrates infrared images during acquisition, causing the image quality to severely deteriorate and affecting subsequent image analysis. To remove stripe noise, an adaptive comb-type notch filtering method is proposed, and a spatial mathematical stripe noise model and the comb-like impulse spectrum characteristics of stripe noise are theoretically and experimentally analyzed. The analysis results demonstrate that when periodic stripe noise consists of only intact stripes, the amplitude spectrum of the periodic stripe noise is an ideal impulse comb spectrum, and the noise frequency energy is concentrated; by contrast, when the periodic stripe noise contains incomplete stripes, spectral leakage occurs, and the noise frequency energy spreads to all frequencies. On this basis, optimal methods of estimating the stripe width and the size of the subimages to be processed are also presented. First, we crop the original image into two subimages of the optimal size to focus the comb-like impulse spectrum characteristics of the stripe noise, thereby enabling easier removal of the stripe noise patterns in the subimages. Then we detect the peaks in the amplitude spectra of the subimages based on the characteristics of the comb-like impulse spectrum and eliminate those peaks using an adaptive comb-type notch filter. Finally, we fuse the two filtered subimages into a final image. Experimental results show that the proposed method achieves a good effect in removing stripe noise from infrared images.

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Jihong Pei, Mi Zou, and Yang Zhao "Adaptive comb-type filtering method for stripe noise removal in infrared images," Journal of Electronic Imaging 28(1), 013037 (18 February 2019). https://doi.org/10.1117/1.JEI.28.1.013037

Received: 15 November 2018; Accepted: 31 January 2019; Published: 18 February 2019

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