Simulation of anisoplanatic lucky look imaging and statistics through optical turbulence using numerical wave propagation: erratum.

We present an erratum to our previously published work [Appl. Opt.60, G19 (2021)APOPAI0003-693510.1364/AO.427716] that corrects errors in the body of the paper. The corrections do not affect the results and conclusion of the original paper.

Atmospheric optical turbulence mitigation using iterative image registration and least squares lucky look fusion.

This paper presents an atmospheric optical turbulence mitigation method that uses a sequence of short-exposure frames. An iterative block matching registration method is proposed for image dewarping. The dewarped frames are combined in a least squares lucky look (LL) fusion process. Here image patches are weighted so as to produce a fused image that is consistent with a theoretical LL optical transfer function (OTF) model. Finally, a Wiener filter is applied to provide deconvolution of the LL OTF. We also explore the LL selectivity tradespace. As the selectivity increases, the LL OTF becomes more favorable but the signal-to-noise ratio suffers, and vice versa. A restoration algorithm is applied to simulated data for quantitative analysis and two different real-world datasets for subjective evaluation. The proposed approach provides improved results compared with the benchmark methods.

Simulation of anisoplanatic lucky look imaging and statistics through optical turbulence using numerical wave propagation.

This paper investigates anisoplanatic numerical wave simulation in the context of lucky look imaging. We demonstrate that numerical wave propagation can produce root mean square (RMS) wavefront distributions and probability of lucky look (PLL) statistics that are consistent with Kolmogorov theory. However, the simulated RMS statistics are sensitive to the sampling parameters used in the propagation window. To address this, we propose and validate a new sample spacing rule based on the point source bandwidth used in the propagation and the level of atmospheric turbulence. We use the tuned simulator to parameterize the wavefront RMS probability density function as a function of turbulence strength. The fully parameterized RMS distribution model is used to provide a way to accurately predict the PLL for a range of turbulence strengths. We also propose and validate a new parametric average lucky look optical transfer function (OTF) model that could be used to aid in image restoration. Our OTF model blends the theoretical diffraction-limited OTF and the average turbulence short exposure OTF. Finally, we show simulated images for several anisoplanatic imaging scenarios that reveal the spatially varying nature of the RMS values impacting local image quality.

Single-pulse, Kerr-effect Mueller matrix LiDAR polarimeter.

We present a novel light detection and ranging (LiDAR) polarimeter that enables measurement of 12 of 16 sample Mueller matrix elements in a single, 10 ns pulse. The new polarization state generator (PSG) leverages Kerr phase modulation in a birefringent optical fiber, creating a probe pulse characterized by temporally varying polarization. Theoretical expressions for the Polarization State Generator (PSG) Stokes vector are derived for birefringent walk-off and no walk-off and incorporated into a time-dependent polarimeter signal model employing multiple polarization state analyzers (PSA). Polarimeter modeling compares the Kerr effect and electro-optic phase modulator-based PSG using a single Polarization State Analyzer (PSA) and a scattering sample yielding similarly good performance for both. We include results from an experimental demonstration of the Kerr effect PSG.

Human skin detection in the visible and near infrared.

Skin detection is a well-studied area in color imagery and is useful in a number of scenarios to include search and rescue and computer vision. Most approaches focus on color imagery due to cost and availability. Many of the visible-based approaches do well at detecting skin (above 90%) but they tend to have relatively high false-alarm rates (8%-15%). This article presents a novel feature space for skin detection in visible and near infrared portions of the electromagnetic spectrum. The features are derived from known spectral absorption of skin constituents to include hemoglobin, melanin, and water as well as scattering properties of the dermis. Fitting a Gaussian mixture to skin and background distributions and using a likelihood ratio test detector, the features presented here show dominating performance when comparing receiver-operating characteristic curves (ROCs) and statistically significant improvement when comparing equal error rates and area under the ROC (AUC). A detection/false-alarm probability of 98.6%/1.1% is achieved for the averaged equal error rate (EER). EER values for the proposed feature space show a 5.6%-11.2% increase in detection probability with a 6.0%-11.6% decrease in false-alarm probability compared to well performing color-based features. The AUC shows a 0.034-0.173 increase in total area under the curve compared to well performing color-based features.

Range separation performance and optimal pulse-width prediction of a three-dimensional flash laser detection and ranging using the Cramer-Rao bound.

This paper derives the Cramer-Rao bound (CRB) on range separation estimation of two point sources interrogated by a three-dimensional flash laser detection and ranging (LADAR) system. An unbiased range separation estimator is also derived to compare against the bound. Additionally, the CRB can be expressed as a function of two LADAR design parameters (range sampling and transmitted pulse width), which can be selected in order to optimize the expected range resolution between two point sources. Given several range sampling capabilities, the CRB and simulation show agreement that there is an optimal pulse width where a shorter pulse width would increase estimation variance due to undersampling of the pulse and a longer pulse width would degrade the resolving capability. Finally, the optimal pulse-width concept is extended to more complex targets and a normalized pulse definition.

Cardinal interpolation.

A Bayesian probability density for an interpolating function is developed, and its desirable properties and practical potential are demonstrated. This density has an often needed but previously unachieved property, here called cardinal interpolation, which ensures extrapolation to the density of the least squares linear model. In particular, the mean of the cardinal interpolation density is a smooth function that intersects given (x, y) points and which extrapolates to their least squares line, and the variance of this density is a smooth function that is zero at the point x values, that increases with distance from the nearest point x value, and that extrapolates to the well-known quadratic variance function for the least squares line. The new cardinal interpolation density is developed for Gaussian radial basis interpolators using fully Bayesian methods that optimize interpolator smoothness. This optimization determines the basis function widths and yields an interpolating density that is non-Gaussian except for large magnitude x and which is therefore not the outcome of a Gaussian process. Further new development shows that the salient property of extrapolation to the density of the least squares linear model can be achieved for more general approximating (not just interpolating) functions.