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More than Fifty Shades of Grey: Quantitative Characterization of Defects and Interpretation Using SNR and CNR

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Abstract

The quantitative characterization of defects in images is commonly performed using the signal-to-noise ratio (SNR). However, there is a strong debate about this measure. First, because there is no single accepted definition of SNR. Second, because the SNR measurements are highly affected by the regions used to estimate the power of the signal and noise in the image. This work provides an overview of some of the most commonly used SNR measures. Images with different sources of noise, and defects with different contrasts, are used to evaluate and compare the ability of these measures to quantitatively characterize defects. The measures are also evaluated when the images are transformed using common image processing operations, including filtering and gamma correction. This work also proposes a methodology to define the regions used to estimate the power of the signal and noise in the images. Two alternative procedures are proposed weather prior information is available about the inspected specimen or not. The proposed methodology is applied on real data from infrared testing, where the considered SNR measures are evaluated.

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Acknowledgements

This research work has been partially funded by the Spanish National Program for Mobility of Professors and Researchers at an International Level, Reference PRX17/00031.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Oviedo, Campus de Viesques, 33204, Gijón, Spain

    R. Usamentiaga

  2. Computer Vision and Systems Laboratory, Laval University, Quebec, QC, Canada

    C. Ibarra-Castanedo & X. Maldague

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  1. R. Usamentiaga
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  2. C. Ibarra-Castanedo
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  3. X. Maldague
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Correspondence to R. Usamentiaga.

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Usamentiaga, R., Ibarra-Castanedo, C. & Maldague, X. More than Fifty Shades of Grey: Quantitative Characterization of Defects and Interpretation Using SNR and CNR. J Nondestruct Eval 37, 25 (2018). https://doi.org/10.1007/s10921-018-0479-z

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  • DOI: https://doi.org/10.1007/s10921-018-0479-z

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