Daniel A. LeMaster

Super-Resolution for Imagery from Integrated Microgrid Polarimeters

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.

Improved microgrid arrangement for integrated imaging polarimeters.

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.

Mid-Wave Infrared 3D Integral Imaging at Long Range

Integral imaging is an established method for passive three-dimensional (3D) image formation, visualization, and ranging. The applications of integral imaging include significantly improved scene segmentation and the ability to visualize occluded objects. Past demonstrations of this technique have been mainly conducted over short ranges achievable in the laboratory. In this paper, we demonstrate 3D computational integral imaging for ranges out to 2 km using multiple looks from a single moving mid-wave infrared (MWIR) imager. We also demonstrate 3D visualization of occluded objects at ranges over 200 m. To our knowledge, this paper is the first such demonstration at these ranges and the first example of this technique using a mid wave IR imaging system. In addition to presenting results, we also outline our new approach for overcoming the technical challenges unique to long range applications of integral imaging. Future applications of long range 3D integral imaging may include aerospace, search and rescue, satellite 3D imaging, etc.

Aerosol modulation transfer function model for passive long-range imaging over a nonuniform atmospheric path

Abstract. 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 scene-derived 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.

Detection and tracking of RC model aircraftin LWIR microgrid polarimeter data

The LWIR microgrid Polarized InfraRed Advanced Tactical Experiment (PIRATE) sensor was used to image several types of RC model aircraft at varying ranges and speeds under different background conditions. The data were calibrated and preprocessed using recently developed microgrid processing algorithms prior to estimation of the thermal (s0) and polarimetric (s1 and s2) Stokes vector images. The data were then analyzed to assess the utility of polarimetric information when the thermal s0 data is augmented with s1 and s2 information for several model aircraft detection and tracking scenarios. Multi-variate analysis tools were applied in conjunction with multi-hypothesis detection schemes to assess detection performance of the aircraft under different background clutter conditions. We find that polarization is able to improve detection performance when compared with the corresponding thermal data in nearly all cases. A tracking algorithm was applied to a sequence of s0 and corresponding degree of linear polarization (DoLP) images. An initial assessment was performed to determine whether polarization information can provide additional utility in these tracking scenarios.

Simulation of Anisoplanatic Imaging through Optical Turbulence Using Numerical Wave Propagation with New Validation Analysis

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 Cn2(z) 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.

Stokes image reconstruction for two-color microgrid polarization imaging systems

The Air Force Research Laboratory has developed a new microgrid polarization imaging system capable of simultaneously reconstructing linear Stokes parameter images in two colors on a single focal plane array. In this paper, an effective method for extracting Stokes images is presented for this type of camera system. It is also shown that correlations between the color bands can be exploited to significantly increase overall spatial resolution. Test data is used to show the advantages of this approach over bilinear interpolation. The bounds (in terms of available reconstruction bandwidth) on image resolution are also provided.

Adaptive scene-based correction algorithm for removal of residual fixed pattern noise in microgrid image data

Pixel-to-pixel response nonuniformity is a common problem that affects nearly all focal plane array sensors. This results in a frame-to-frame fixed pattern noise (FPN) that causes an overall degradation in collected data. FPN is often compensated for through the use of blackbody calibration procedures; however, FPN is a particularly challenging problem because the detector responsivities drift relative to one another in time, requiring that the sensor be recalibrated periodically. The calibration process is obstructive to sensor operation and is therefore only performed at discrete intervals in time. Thus, any drift that occurs between calibrations (along with error in the calibration sources themselves) causes varying levels of residual calibration error to be present in the data at all times. Polarimetric microgrid sensors are particularly sensitive to FPN due to the spatial differencing involved in estimating the Stokes vector images. While many techniques exist in the literature to estimate FPN for conventional video sensors, few have been proposed to address the problem in microgrid imaging sensors. Here we present a scene-based nonuniformity correction technique for microgrid sensors that is able to reduce residual fixed pattern noise while preserving radiometry under a wide range of conditions. The algorithm requires a low number of temporal data samples to estimate the spatial nonuniformity and is computationally efficient. We demonstrate the algorithms performance using real data from the AFRL PIRATE and University of Arizona LWIR microgrid sensors.

Comparative analysis of numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence

Abstract. Computational efficiency and accuracy of wave-optics-based Monte–Carlo and brightness function numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence are evaluated. Simulation results are compared with theoretical estimates based on known analytical solutions for the modulation transfer function of an imaging system and the long-exposure image of a Gaussian-shaped incoherent light source. It is shown that the accuracy of both techniques is comparable over the wide range of path lengths and atmospheric turbulence conditions, whereas the brightness function technique is advantageous in terms of the computational speed.

SWIR active polarization imaging for material identification

Nighttime active SWIR imaging has resolution, size, weight, and power consumption advantages over passive MWIR and LWIR imagers for applications involving target identification. We propose that the target discrimination capability of active SWIR systems can be extended further by exerting polarization control over the illumination source and imager, i.e. through active polarization imaging. In this work, we construct a partial Mueller matrix imager and use laboratory derived signatures to uniquely identify target materials in outdoor scenes. This paper includes a description of the camera and laser systems as well as discussion of the reduction and analysis techniques used for material identification.