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Volume: 29 | Article ID: art00018
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The Effects of misregistration on the dead leaves crosscorrelation texture blur analysis
  DOI :  10.2352/ISSN.2470-1173.2017.12.IQSP-257  Published OnlineJanuary 2017
Abstract

The dead leaves image model is often used for measurement of the spatial frequency response (SFR) of digital cameras, where response to fine texture is of interest. It has a power spectral density (PSD) similar to natural images and image features of varying sizes, making it useful for measuring the texture-blurring effects of non-linear noise reduction which may not be well analyzed by traditional methods. The standard approach for analyzing images of this model is to compare observed PSDs to the analytically known one. However, recent works have proposed a cross-correlation based approach which promises more robust measurements via full-reference comparison with the known true pattern. A major assumption of this method is that the observed image and reference image can be aligned (registered) with subpixel accuracy. In this paper we study the effects of registration errors on the calculation of texture-based SFR and its derivative metrics (such as MTF50), in order to determine how accurate this registration must be for reliable results. We also propose a change to the dead leaves cross-correlation algorithm, recommending the use of the absolute value of the transfer function rather than its real part. Simulations of registration error on both real and simulated observed images reveal that small amounts of misregistration (as low as 0.15px) can cause large variability in MTF curves derived using the real part of the transfer function, while MTF curves derived from the absolute value are significantly less affected.

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  Cite this article 

Robert C Sumner, Ranga Burada, Noah Kram, "The Effects of misregistration on the dead leaves crosscorrelation texture blur analysisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XIV,  2017,  pp 121 - 129,  https://doi.org/10.2352/ISSN.2470-1173.2017.12.IQSP-257

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