Application of tilt correlation statistics to anisoplanatic optical turbulence modeling and mitigation

Atmospheric optical turbulence can be a significant source of image degradation, particularly in long range imaging applications. Many turbulence mitigation algorithms rely on an optical transfer function (OTF) model that includes the Fried parameter. We present anisoplanatic tilt statistics for spherical wave propagation. We transform these into 2D autocorrelation functions that can inform turbulence modeling and mitigation algorithms. Using these, we construct an OTF model that accounts for image registration. We also propose a spectral ratio Fried parameter estimation algorithm that is robust to camera motion and requires no specialized scene content or sources. We employ the Fried parameter estimation and OTF model for turbulence mitigation. A numerical wave-propagation turbulence simulator is used to generate data to quantitatively validate the proposed methods. Results with real camera data are also presented

Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as the block matching and CNN (BM-CNN) method. Training the CNN is accomplished using simulated data from a fast turbulence simulation tool capable of producing a large amount of degraded imagery from declared truth images rapidly. Testing is done using independent data simulated with a different well-validated numerical wave-propagation simulator. Our proposed BM-CNN TM method is evaluated in a number of experiments using quantitative metrics. The quantitative analysis is made possible by virtue of having truth imagery from the simulations. A number of restored images are provided for subjective evaluation. We demonstrate that the BM-CNN TM method outperforms the benchmark methods in the scenarios tested

Super-resolution in the presence of atmospheric optical turbulence

The design of imaging systems involves navigating a complex trade space. As a result, many imaging systems employ focal plane arrays with a detector pitch that is insufficient to meet the Nyquist sampling criterion under diffraction-limited imaging conditions. This undersampling may result in aliasing artifacts and prevent the imaging system from achieving the full resolution afforded by the optics. Another potential source of image degradation, especially for long-range imaging, is atmospheric optical turbulence. Optical turbulence gives rise to spatially and temporally varying image blur and warping from fluctuations in the index of refraction along with optical path. Under heavy turbulence, the blurring from the turbulence acts as an anti-aliasing filter, and undersampling does not generally occur. However, under light to moderate turbulence, many imaging systems will exhibit both aliasing artifacts and turbulence degradation. Few papers in the literature have analyzed or addressed both of these degradations together. In this paper, we provide a novel analysis of undersampling in the presence of optical turbulence. Specifically, we provide an optical transfer function analysis that illustrates regimes where aliasing and turbulence are both present, and where they are not. We also propose and evaluate a super-resolution (SR) method for combating aliasing that offers robustness to optical turbulence. The method has a tuning parameter that allows it to transition from traditional diffraction-limited SR, to pure turbulence mitigation with no SR. The proposed method is based on Fusion of Interpolated Frames (FIF) SR, recently proposed by two of the current authors. We quantitatively evaluate the SR method with varying levels of optical turbulence using simulated sequences. We also presented results using real infrared imagery

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this task. We propose a variation of the FIF SR method that has a fusion parameter that allows it to transition from traditional diffraction-limited SR to pure TM with no SR as well as a continuum in between. This fusion parameter balances subpixel resolution, needed for SR, with the amount of temporal averaging, needed for TM and noise reduction. In addition, we develop a model of the interpolation blurring that results from the fusion process, as a function of this tuning parameter. The blurring model is then incorporated into the overall degradation model that is addressed in the restoration step of the FIF SR method. This innovation benefits the FIF SR method in all applications. We present a number of experimental results to demonstrate the efficacy of the FIF SR method in different levels of turbulence. Simulated imagery with known ground truth is used for a detailed quantitative analysis. Three real infrared image sequences are also used. Two of these include bar targets that allow for a quantitative resolution enhancement assessment

Differential Tilt Variance Effects of Turbulence in Imagery: Comparing Simulation with Theory

Differential tilt variance is a useful metric for interpreting the distorting effects of turbulence in incoherent imaging systems. In this paper, we compare the theoretical model of differential tilt variance to simulations. Simulation is based on a Monte Carlo wave optics approach with split step propagation. Results show that the simulation closely matches theory. The results also show that care must be taken when selecting a method to estimate tilts

SARS-CoV-2/COVID-19 Testing: The Tower of Babel

Background and aim: Testing represents one of the main pillars of public health response to SARS-CoV-2/COVID-19 pandemic. This paper shows how accuracy and utility of testing programs depend not just on the type of tests, but on the context as well. Methods: We describe the testing methods that have been developed and the possible testing strategies; then, we focus on two possible methods of population-wide testing, i.e., pooled testing and testing with rapid antigen tests. We show the accuracy of split-pooling method and how, in different pre-test probability scenarios, the positive and negative predictive values vary using rapid antigen tests. Results: Split-pooling, followed by retesting of negative results, shows a higher sensitivity than individual testing and requires fewer tests. In case of low pre-test probability, a negative result with antigen test could allow to rule out the infection, while, in case of a positive result, a confirmatory molecular test would be necessary. Conclusions: Test performance alone is not enough to properly choose which test to use; goals and context of the testing program are essential. We advocate the use of pooled strategies when planning population-wide screening, and the weekly use of rapid tests for close periodic monitoring in low-prevalence populations

A Holistic Registration Approach to Fusion of Interpolated Frames

The Fusion of Interpolated Frames Super-Resolution (FIF SR) method has successfully been used restore turbulent aliased imagery with the aid of only a global affine registration routine. This paper examines an extension of this method that employs both a global affine registration and a local block-matching optical flow registration to better address local warping in turbulent imagery. Results are presented from simulated datasets that have varying levels of optical turbulence

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

Atmospheric Optical Turbulence Mitigation using Iterative Image Registration and Least Squares Lucky Look Fusion

Excerpt: 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. © Optica Publishing Group