, "Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis," Opt. Eng. 56(7), 071502 (2017), doi: 10.1117/1.OE.56.7.071502. Abstract. We present a numerical wave propagation method for simulating imaging of an extended scene under anisoplanatic conditions. While isoplanatic simulation is relatively common, few tools are specifically designed for simulating the imaging of extended scenes under anisoplanatic conditions. We provide a complete description of the proposed simulation tool, including the wave propagation method used. Our approach computes an array of point spread functions (PSFs) for a two-dimensional grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. The degradation includes spatially varying warping and blurring. To produce the PSF array, we generate a series of extended phase screens. Simulated point sources are numerically propagated from an array of positions on the object plane, through the phase screens, and ultimately to the focal plane of the simulated camera. Note that the optical path for each PSF will be different, and thus, pass through a different portion of the extended phase screens. These different paths give rise to a spatially varying PSF to produce anisoplanatic effects. We use a method for defining the individual phase screen statistics that we have not seen used in previous anisoplanatic simulations. We also present a validation analysis. In particular, we compare simulated outputs with the theoretical anisoplanatic tilt correlation and a derived differential tilt variance statistic. This is in addition to comparing the long-and short-exposure PSFs and isoplanatic angle. We believe this analysis represents the most thorough validation of an anisoplanatic simulation to date. The current work is also unique that we simulate and validate both constant and varying C 2 n ðzÞ profiles. Furthermore, we simulate sequences with both temporally independent and temporally correlated turbulence effects. Temporal correlation is introduced by generating even larger extended phase screens and translating this block of screens in front of the propagation area. Our validation analysis shows an excellent match between the simulation statistics and the theoretical predictions. Thus, we think this tool can be used effectively to study optical anisoplanatic turbulence and to aid in the development of image restoration methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

A maximum likelihood blind deconvolution algorithm is derived for incoherent polarimetric imagery using expectation maximization. In this approach, the unpolarized and fully polarized components of the scene are estimated along with the corresponding angles of polarization and channel point spread functions. The scene state of linear polarization is determined unambiguously using this parameterization. Results are demonstrated using laboratory data.

An aerosol modulation transfer function (MTF) model is developed to assess the impact of aerosol scattering on passive long-range imaging sensors. The methodology extends from previous work to explicitly address imaging scenarios with a nonuniform distribution of scattering characteristics over the propagation path and incorporates the moderate resolution transfer code database of aerosol cross-section and phase function characteristics in order to provide an empirical foundation for realistic quantitative MTF assessments. The resulting model is compared with both predictions from a Monte-Carlo scattering simulation and a scenederived MTF estimate from an empirical image, with reasonable agreement in both cases. Application to long-range imaging situations at both visible and infrared wavelengths indicates that the magnitude and functional form of the aerosol MTF differ significantly from other contributors to the composite system MTF. Furthermore, the image-quality impact is largely radiometric in the sense that the contrast reduction is approximately independent of spatial frequency, and image blur is practically negligible.

Imagery from microgrid polarimeters is obtained by using a mosaic of pixel-wise micropolarizers on a focal plane array (FPA). Each distinct polarization image is obtained by subsampling the full FPA image. Thus, the effective pixel pitch for each polarization channel is increased and the sampling frequency is decreased. As a result, aliasing artifacts from such undersampling can corrupt the true polarization content of the scene. Here we present the first multi-channel multi-frame super-resolution (SR) algorithms designed specifically for the problem of image restoration in microgrid polarization imagers. These SR algorithms can be used to address aliasing and other degradations, without sacrificing field of view or compromising optical resolution with an anti-aliasing filter. The new SR methods are designed to exploit correlation between the polarimetric channels. One of the new SR algorithms uses a form of regularized least squares and has an iterative solution. The other is based on the faster adaptive Wiener filter SR method. We demonstrate that the new multi-channel SR algorithms are capable of providing significant enhancement of polarimetric imagery and that they outperform their independent channel counterparts.

For almost 20 years, microgrid polarimetric imaging systems have been built using a 2×2 repeating pattern of polarization analyzers. In this Letter, we show that superior spatial resolution is achieved over this 2×2 case when the analyzers are arranged in a 2×4 repeating pattern. This unconventional result, in which a more distributed sampling pattern results in finer spatial resolution, is also achieved without affecting the conditioning of the polarimetric data-reduction matrix. Proof is provided theoretically and through Stokes image reconstruction of synthesized data.