High-resolution ab initio three-dimensional X-ray diffraction microscopy

Coherent X-ray diffraction microscopy is a method of imaging non-periodic isolated objects at resolutions only limited, in principle, by the largest scattering angles recorded. We demonstrate X-ray diffraction imaging with high resolution in all three dimensions, as determined by a quantitative analysis of the reconstructed volume images. These images are retrieved from the 3D diffraction data using no a priori knowledge about the shape or composition of the object, which has never before been demonstrated on a non-periodic object. We also construct 2D images of thick objects with infinite depth of focus (without loss of transverse spatial resolution). These methods can be used to image biological and materials science samples at high resolution using X-ray undulator radiation, and establishes the techniques to be used in atomic-resolution ultrafast imaging at X-ray free-electron laser sources.Comment: 22 pages, 11 figures, submitte

Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction

The ultimate goal of many image-based modeling systems is to render photo-realistic novel views of a scene without visible artifacts. Existing evaluation metrics and benchmarks focus mainly on the geometric accuracy of the reconstructed model, which is, however, a poor predictor of visual accuracy. Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry. Examples include light field or image-based rendering systems. We propose a unified evaluation approach based on novel view prediction error that is able to analyze the visual quality of any method that can render novel views from input images. One of the key advantages of this approach is that it does not require ground truth geometry. This dramatically simplifies the creation of test datasets and benchmarks. It also allows us to evaluate the quality of an unknown scene during the acquisition and reconstruction process, which is useful for acquisition planning. We evaluate our approach on a range of methods including standard geometry-plus-texture pipelines as well as image-based rendering techniques, compare it to existing geometry-based benchmarks, and demonstrate its utility for a range of use cases.Comment: 10 pages, 12 figures, paper was submitted to ACM Transactions on Graphics for revie

A note on the depth-from-defocus mechanism of jumping spiders

Jumping spiders are capable of estimating the distance to their prey relying only on the information from one of their main eyes. Recently, it has been shown that jumping spiders perform this estimation based on image defocus cues. In order to gain insight into the mechanisms involved in this blur-to-distance mapping as performed by the spider and to judge whether inspirations can be drawn from spider vision for depth-from-defocus computer vision algorithms, we constructed a three-dimensional (3D) model of the anterior median eye of the Metaphidippus aeneolus, a well studied species of jumping spider. We were able to study images of the environment as the spider would see them and to measure the performances of a well known depth-from-defocus algorithm on this dataset. We found that the algorithm performs best when using images that are averaged over the considerable thickness of the spider's receptor layers, thus pointing towards a possible functional role of the receptor thickness for the spider's depth estimation capabilities

Design and optimization of 2.5 dimension porous media micromodel for nanosensor flow experiments

Micromodels are used to visualize and study pore-scale phenomena such as immiscible displacements in porous media, foam flow behavior, and CO2 flooding. The understanding gained from these experiments can be used to develop models to predict future behavior of the reservoir. Most micromodels are constructed using lithography techniques that are restricted to 2D patterns that require artificial generation or manipulation of images to develop connected micromodels. Characteristics innate to the original rock structure are often lost or skewed in developing micromodels that bear little resemblance to the original media. Alternative microfabrication techniques using a micromilling tool have allowed us to vary the floor height in a micromodel, thus giving some variation in the third dimension. We refer to these structures (with varying floor height and fixed ceiling, and which cannot have passages on top of one another) as 2.5D micromodels. Using a technique called depth averaging, in which we take a section of a 3D voxel image of porous media and project the solid voxels down while simultaneously pushing the void space above, we generate micromodels that may allow for more accurate representations of the pore structure in 3D rock. The design of the etched pattern requires the selection of a specific depth (or number of XMCT image slice) over which to average the image data. The 2.5-D pattern was obtained by optimizing a series of parameters to ensure the structure and flow patterns matched as closely as possible to the equivalent 3D structure and flow as can be accommodated given the restricted dimensionality. Parameters considered include flow-based parameters, common statistical correlations, and a host of topological parameters obtained by network model generation techniques. For a Boise sandstone core sample imaged at 5.07 µm/pixel, an optimized depth of 115 µm gave the most accurate measures across the range of parameters. However, due to constraints regarding the resolution of the micromilling process, a second series of flow simulations were conducted in the originally optimized region of interest (100-150 µm) for a lower resolution image that resulted in the selection of 130 µm to depth average. This design was then used to fabricate a brass mold insert. The process of developing the microchips for nanosensor experiments is currently in the stage of assembling the PMMA chips