Multi-Layer Overlapped Subaperture Algorithm for Extremely-High-Squint High-Resolution Wide-Swath SAR Imaging with Continuously Time-Varying Radar Parameters

Extremely-high-squint (EHS) geometry of the traditional constant-parameter synthetic aperture radar (SAR) induces non-orthogonal wavenumber spectrum and hence the distortion of point spread function (PSF) in focused images. The method invented to overcome this problem is referred to as new-concept parameter-adjusting SAR. It corrects the PSF distortion by adjusting radar parameters, such as carrier frequency and chirp rate, based on instant data acquisition geometry. In this case, the characteristic of signal is quite different from the constant-parameter SAR and therefore, the traditional imaging algorithms cannot be directly applied for parameter-adjusting SAR imaging. However, the existing imaging algorithm for EHS parameter-adjusting SAR suffers from insufficient accuracy if a high-resolution wide-swath (HRWS) performance is required. Thus, this paper proposes a multi-layer overlapped subaperture algorithm (ML-OSA) for EHS HRWS parameter-adjusting SAR imaging with three main contributions: First, a more accurate signal model with time-varying radar parameters in high-squint geometry is derived. Second, phase errors are compensated with much higher accuracy by implementing multiple layers of coarse-to-fine spatially variant filters. Third, the analytical swath limit of the ML-OSA is derived by considering both the residual errors of signal model and phase compensations. The presented approach is validated via both the point- and extended-target computer simulations.

Penalized-Likelihood PET Image Reconstruction Using Similarity-Driven Median Regularization

In this paper, we present a new regularized image reconstruction method for positron emission tomography (PET), where an adaptive weighted median regularizer is used in the context of a penalized-likelihood framework. The motivation of our work is to overcome the limitation of the conventional median regularizer, which has proven useful for tomographic reconstruction but suffers from the negative effect of removing fine details in the underlying image when the edges occupy less than half of the window elements. The crux of our method is inspired by the well-known non-local means denoising approach, which exploits the measure of similarity between the image patches for weighted smoothing. However, our method is different from the non-local means denoising approach in that the similarity measure between the patches is used for the median weights rather than for the smoothing weights. As the median weights, in this case, are spatially variant, they provide adaptive median regularization achieving high-quality reconstructions. The experimental results indicate that our similarity-driven median regularization method not only improves the reconstruction accuracy, but also has great potential for super-resolution reconstruction for PET.

Interpreting Observed Temperature Probability Distributions Using a Relationship between Temperature and Temperature Advection

The non-normality of temperature probability distributions and the physics that drive it are important due to their relationships to the frequency of extreme warm and cold events. Here we use a conditional mean framework to explore how horizontal temperature advection and other physical processes work together to control the shape of daily temperature distributions during 1979-2019 in the ERA5 reanalysis for both JJA and DJF. We demonstrate that the temperature distribution in mid- and high- latitudes can largely be linearly explained by the conditional mean horizontal temperature advection with the simple treatment of other processes as a Newtonian relaxation with a spatially-variant relaxation time scale and equilibrium temperature. We analyze the role of different transient and stationary components of the horizontal temperature advection in affecting the shape of temperature distributions. The anomalous advection of the stationary temperature gradient has a dominant effect in influencing temperature variance, while both that term and the covariance between anomalous wind and anomalous temperature have significant effects on temperature skewness. While this simple method works well over most of the ocean, the advection-temperature relationship is more complicated over land. We classify land regions with different advection-temperature relationships under our framework, and find that for both seasons the aforementioned linear relationship can explain ~30% of land area, and can explain either the lower or the upper half of temperature distributions in an additional ~30% of land area. Identifying the regions where temperature advection explains shapes of temperature distributions well will help us gain more confidence in understanding the future change of temperature distributions and extreme events.

Foveated glasses-free 3D display with ultrawide field of view via a large-scale 2D-metagrating complex

Glasses-free three-dimensional (3D) displays are one of the game-changing technologies that will redefine the display industry in portable electronic devices. However, because of the limited resolution in state-of-the-art display panels, current 3D displays suffer from a critical trade-off among the spatial resolution, angular resolution, and viewing angle. Inspired by the so-called spatially variant resolution imaging found in vertebrate eyes, we propose 3D display with spatially variant information density. Stereoscopic experiences with smooth motion parallax are maintained at the central view, while the viewing angle is enlarged at the periphery view. It is enabled by a large-scale 2D-metagrating complex to manipulate dot/linear/rectangular hybrid shaped views. Furthermore, a video rate full-color 3D display with an unprecedented 160° horizontal viewing angle is demonstrated. With thin and light form factors, the proposed 3D system can be integrated with off-the-shelf purchased flat panels, making it promising for applications in portable electronics.

Bio-inspired spatially variant photonic crystals for self-collimation and beam-steering applications in the near-infrared spectrum

The self-collimation of light through Photonic Crystals (PCs) due to their optical properties and through a special geometric structure offers a new form of beam steering with highly optical control capabilities for a range of different applications. The objective of this work is to understand self-collimation and bending of light beams through bio-inspired Spatially Variant Photonic Crystals (SVPCs) made from dielectric materials such as silicon dioxide and common polymers used in three-dimensional printing like SU-8. Based upon natural PCs found in animals such as butterflies and fish, the PCs developed in this work can be used to manipulate different wavelengths of light for optical communications, multiplexing, and beam-tuning devices for light detection and ranging applications. In this paper, we show the optical properties and potential applications of two different SVPC designs that can control light through a 90-degree bend and optical logic gates. These two-dimensional SVPC designs were optimized for operation in the near-infrared range of approximately 800–1000 nm for the 90-degree bend and 700–1000 nm for the optical logic gate. These SVPCs were shown to provide high transmission through desired regions with low reflection and absorption of light to prove the potential benefits of these structures for future optical systems.