Abstract
Speckle-tracking imaging has the advantages of simple setup and high-sensitivity to slowly varying phase gradients. Subset size choice is regarded as a trade-off problem for speckle-tracking X-ray imaging where one needs to balance the spatial resolution and accuracy, where the subset was defined as the region of interest of windowing choice for digital image correlation algorithm. An adaptive subset size choice method based on a Fourier transform for effectively detecting sample phase information without foreknowledge of the sample structure is presented in this study. The speckle-tracking phase-contrast and the form of dark-field imaging based on this method have the advantages of (i) high resolution and time saving compared to large subset choice and (ii) partially improvement the influence from experimental noises, background fluctuations, and false signals compared to small subset choice at the same time. This method has proven to be particularly robust in the experimental condition of poor signal-to-noise ratio. The proposed method may be expanded to all speckle-based imaging methods and other imaging techniques based on the subset or window matching.
Highlights
The speckle-based X-ray imaging technique[1,2,3] has been developed recently due to its many advantages compared to conventional propagation-based[4] or grating-based[5] imaging techniques
The first common downside is that speckle-based imaging is insensitive to high-frequency features, but this can be partially improved by removing the effect of free-space propagation from the speckle pattern in order to produce an edge enhancement[13]
Pan et al used the sum of the square of the subset intensity gradient (SSSIG) as a criterion to select the subset size[15], but for a complicated sample this study did not design any thresholds for the criterion to suit different positions
Summary
The speckle-based X-ray imaging technique[1,2,3] has been developed recently due to its many advantages compared to conventional propagation-based[4] or grating-based[5] imaging techniques. The demonstration of searching process for MCCC in the cross-correlation map with different subset choices inside and at the edge of the sample is shown in the Fig. 2.
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