We develop a means for speckle-based phase imaging of the projected thickness of a single-material object, under the assumption of illumination by spatially random time-independent x-ray speckles. These speckles are generated by passing x rays through a suitable spatially random mask. The method makes use of a single image obtained in the presence of the object, which serves to deform the illuminating speckle field relative to a reference speckle field (which only needs to be measured once) obtained in the presence of the mask and the absence of the object. The method implicitly rather than explicitly tracks speckles and utilizes the transport-of-intensity equation to give a closed-form solution to the inverse problem of determining the complex transmission function of the object. Implementation using x-ray synchrotron data shows the method to be robust and efficient with respect to noise. Applications include x-ray phase-amplitude radiography and tomography, as well as time-dependent imaging of dynamic and radiation-sensitive samples using low-flux sources.

The classification and extraction of road markings and lanes are of critical importance to infrastructure assessment, planning and road safety. We present a pipeline for the accurate segmentation and extraction of rural road surface objects in 3D lidar point-cloud, as well as a method to extract geometric parameters belonging to tar seal. To decrease the computational resources needed, the point-clouds were aggregated into a 2D image space before being transformed using affine transformations. The Mask R-CNN algorithm is then applied to the transformed image space to localize, segment and classify the road objects. The segmentation results for road surfaces and markings can then be used for geometric parameter estimation such as road widths estimation, while the segmentation results show that the efficacy of the existing Mask R-CNN to segment needle-type objects is improved by our proposed transformations.

We develop x-ray multi-modal intrinsic-speckle-tracking (MIST), a form of x-ray speckle-tracking that is able to recover both the position-dependent phase shift and the position-dependent small-angle x-ray scattering (SAXS) signal of a phase object. MIST is based on combining a Fokker–Planck description of paraxial x-ray optics, with an optical-flow formalism for x-ray speckle-tracking. Only two images need to be taken in the presence of the sample, corresponding to two different transverse positions of the speckle-generating membrane, in order to recover both the refractive and local-SAXS properties of the sample. Like the optical-flow x-ray phase-retrieval method which it generalises, the MIST method implicitly rather than explicitly tracks both the transverse motion and the diffusion of speckles that is induced by the presence of a sample. Application to x-ray synchrotron data shows the method to be efficient, rapid and stable.

When a macroscopic-sized noncrystalline sample is illuminated using coherent x-ray radiation, a bifurcation of photon energy flow may occur. The coarse-grained complex refractive index of the sample may be considered to attenuate and refract the incident coherent beam, leading to a coherent component of the transmitted beam. Spatially unresolved sample microstructure, associated with the fine-grained components of the complex refractive index, introduces a diffuse component to the transmitted beam. This diffuse photon-scattering channel may be viewed in terms of position-dependent fans of ultrasmall-angle x-ray scatter. These position-dependent fans, at the exit surface of the object, may under certain circumstances be approximated as having a locally elliptical shape. By using an anisotropic-diffusion Fokker-Planck approach to model this bifurcated x-ray energy flow, we show how all three components (attenuation, refraction, and locally elliptical diffuse scatter) may be recovered. This is done via x-ray speckle tracking, in which the sample is illuminated with spatially random x-ray fields generated by coherent illumination of a spatially random membrane. The theory is developed and then successfully applied to experimental x-ray data.

Purpose: We investigate how an intrinsic speckle tracking approach to speckle-based x-ray imaging is used to extract an object’s effective dark-field (DF) signal, which is capable of providing object information in three dimensions. Approach: The effective DF signal was extracted using a Fokker–Planck type formalism, which models the deformations of illuminating reference beam speckles due to both coherent and diffusive scatter from the sample. Here, we assumed that (a) small-angle scattering fans at the exit surface of the sample are rotationally symmetric and (b) the object has both attenuating and refractive properties. The associated inverse problem of extracting the effective DF signal was numerically stabilized using a “weighted determinants” approach. Results: Effective DF projection images, as well as the DF tomographic reconstructions of the wood sample, are presented. DF tomography was performed using a filtered back projection reconstruction algorithm. The DF tomographic reconstructions of the wood sample provided complementary, and otherwise inaccessible, information to augment the phase contrast reconstructions, which were also computed. Conclusions: An intrinsic speckle tracking approach to speckle-based imaging can tomographically reconstruct an object’s DF signal at a low sample exposure and with a simple experimental setup. The obtained DF reconstructions have an image quality comparable to alternative x-ray DF techniques.

Near-field x-ray refraction (phase) contrast is unavoidable in many lab-based micro-CT imaging systems. Quantitative analysis of x-ray refraction (a.k.a. phase retrieval) is in general an under-constrained problem. Regularizing assumptions may not hold true for interesting samples; popular single-material methods are inappropriate for heterogeneous samples, leading to undesired blurring and/or over-sharpening. In this paper, we constrain and solve the phase-retrieval problem for heterogeneous objects, using the Alvarez–Macovski model for x-ray attenuation. Under this assumption we neglect Rayleigh scattering and pair production, considering only Compton scattering and the photoelectric effect. We formulate and test the resulting method to extract the material properties of density and atomic number from single-distance, dual-energy imaging of both strongly and weakly attenuating multi-material objects with polychromatic x-ray spectra. Simulation and experimental data are used to compare our proposed method with the Paganin single-material phase-retrieval algorithm, and an innovative interpretation of the data-constrained modeling phase-retrieval technique.