This conference presentation was prepared for SPIE Defense + Commercial Sensing, 2023.

We review compressive tomographic estimation strategies with active illumination and discuss how deep image priors may be combined with ptychographic sampling strategies to enable snap shot 3D object estimation with synthetic aperture resolution. We consider coherence requirements for such systems and derive resolution limits and computational requirements.

Scattering severely limits the visual acuity of an imaging system. This talk discusses how diversity in illumination wavelength can be utilized to circumvent the problem of phase randomization in scattered light fields. Amongst other applications, the introduced method allows for holographic measurements of hidden objects through scattering media or around corners, or for interferometric measurements of macroscopic objects with rough surfaces. This is possible as the technique interrogates the scene at two closely spaced optical wavelengths and computationally assembles a complex “synthetic field” at a “synthetic wave,” which is used for further processing. As the synthetic wavelength is the beat wavelength of the two optical wavelengths, it can be picked orders of magnitudes larger, and the computationally assembled synthetic field becomes immune to the deleterious effect of speckle. During the talk, different flavors of the technique will be introduced, including a method to retrieve the complex synthetic field in single-shot.

TBD

Phase contrast X-ray Imaging has the potential to enhance soft material image contrast and improve target detectability in heterogeneous and complex materials. Applications could be in areas such as separating cancers from normal dense tissue in medical imaging and in separating plastics from explosives in baggage and security imaging. We are investigating the benefits of single photon processing detectors in yielding information such as the energy and time of arrival of photons in enabling novel phase imaging and phase retrieval techniques. We will present the recent advances in simulations, system design and computational advances with the use of quantum processing detectors for x-ray phase imaging.

Many tasks in security, medical imaging, materials science, and non-destructive testing require spatially-resolved material identification and/or quantification. X-ray diffraction imaging can accomplish this task, but it has typically been slow or of too course resolution to adequately address the real world need. In this talk, I discuss our computational approach to X-ray diffraction imaging that allows for 2D and 3D X-ray diffraction imaging at speeds relevant to the task at hand. Through a combination of physical coding, model-based reconstruction, inclusion of side-information, and machine learning-based processing, we demonstrate the ability to evaluate the contents of baggage and parcels in seconds as well as perform high resolution material identification of biological samples on the order of a few minutes using conventional, off-the-shelf components.