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.

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.

Experimental evidence for spatial stabilization of deep-turbulence-induced anisoplanatic blur.

We present field-experiment support for the feasibility of post-detection restoration when imaging through deep turbulence characterized by extreme anisoplanatism. Short-exposure images of point-like and minimally extended objects (MEOs) were collected, viewed through a 5.1-kilometer atmospheric path producing isoplanatic angles roughly 1/15th the camera diffraction-limited angular resolution. A correlation-based isoplanatic angle measurement technique is presented along with data verifying deep-turbulence conditions. In agreement with prior wave-optics simulations, the experiments demonstrate short-exposure images of MEOs retain a central lobe that is clearly narrower than the long-exposure counterpart, even in the presence of severe anisoplanatism. New simulations are presented to provide direct comparison with measurements of point-like and MEO image central lobe radius statistics.

Field tests for round-trip imaging at a 1.4 km distance with change detection and ranging using a short-wave infrared super-continuum laser.

Field tests have been conducted of a broadband illuminator for active hyperspectral imaging (HSI) using a short-wave infrared supercontinuum laser (SWIR-SCL). We demonstrated irradiance comparable to the sun for two-way measurements at a 1.4 km distance between laser and target, and performed change detection and ranging. The experimental results suggest that the range resolution of our method is ∼1.5 cm even at the 1.4 km distance. Hence, we demonstrated the possibility to perform HSI with active broadband illumination using the SWIR-SCL. To our knowledge, this experiment is the first-ever to test two-way propagation of the active HSI illumination over a long distance. The 64 W SWIR-SCL provides near sunlight-equivalent illumination over multiple square meters, and the laser could enable HSI 24 h a day, even under a cloud cover, as well as enhanced capabilities such as change detection and ranging.

Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter.

In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.