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Results for “Phase retrieval”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluation of cloud height, optical thickness, and phase retrievals from the CHROMA algorithm applied to Sentinel-3 OLCI data

We previously developed the Cloud Height Retrieval from O 2 Molecular Absorption (CHROMA) algorithm for the Ocean Color Instrument (OCI) on the new NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Here, we apply CHROMA to observations from the Ocean Land Colour Instrument (OLCI) to guide expectations for PACE, as it will take some time to obtain large-scale validation data for OCI. We use cloud top height (CTH), phase, and (for liquid clouds) cloud optical thickness (COT) data from the ground-based Atmospheric Radiation Measurement (ARM) network to evaluate the OLCI retrievals. We found that OLCI and Moderate Resolution Imaging Spectroradiometer (MODIS) CTH compare similarly well to the ARM reference. OLCI has a tendency to underestimate CTH as CTH increases, and algorithm assumptions about cloud geometric thickness may contribute to this. ARM COT from multifilter shadowband radiometers (MFRSR) and Sun photometers are well-correlated with one another, albeit with a roughly 30 % offset on average; OLCI and MODIS COT agree more closely with the MFRSR data. OLCI retrieval uncertainty estimates show skill at telling low-uncertainty cases from high-uncertainty ones, although CTH uncertainties are underestimated. Additionally, we compare the OLCI data to satellite retrievals based on thermal infrared measurements from MODIS and Sea and Land Surface Temperature Radiometer (SLSTR) data. Differences are broadly consistent with physical expectations based on the A-band vs. thermal techniques, although one key challenge in such aggregated comparisons is different cloud masking sensitivities and algorithm failure rates meaning additional sampling differences are introduced. We conclude by discussing the transition to and possible enhancements for PACE OCI.

Sayer, Andrew M. [Univ. of Maryland Baltimore Coun↗
NASA NTRS1992

Automated absolute phase retrieval in across-track interferometry

Discussed is a key element in the processing of topographic radar maps acquired by the NASA/JPL airborne synthetic aperture radar configured as an across-track interferometer (TOPSAR). TOPSAR utilizes a single transmit and two receive antennas; the three-dimensional target location is determined by triangulation based on a known baseline and two measured slant ranges. The slant range difference is determined very accurately from the phase difference between the signals received by the two antennas. This phase is measured modulo 2pi, whereas it is the absolute phase which relates directly to the difference in slant range. It is shown that splitting the range bandwidth into two subbands in the processor and processing each individually allows for the absolute phase. The underlying principles and system errors which must be considered are discussed, together with the implementation and results from processing data acquired during the summer of 1991.

Madsen, Soren N.↗
DOE OSTI2016

Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

AI-enabled high-resolution scanning coherent diffraction imaging

Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.

47 OTHER INSTRUMENTATION↗