Accurate measurement of laser light phase after propagation through underwater optical turbulence is crucial for defense and commercial applications like underwater communications and sensing. Traditional phase-measuring methods, like Shack-Hartmann wavefront sensors, have limited effectiveness in strong optical turbulence. The Gerchberg-Saxton (GS) method utilizes synchronized intensity images in the image and Fourier planes and retrieves the phase through an iterative algorithm. We evaluate the Gerchberg-Saxton algorithm's accuracy for laser light propagation through simulated Kolmogorov turbulence and experimentally generated Rayleigh-Bénard (RB) natural convection. The results of the phase retrieved from the experimental data recorded in pupil and focal planes are compared with the phase measurements from a Shack-Hartmann sensor. We tested the efficacy of the Gerchberg-Saxton algorithm to estimate the phase of laser light upon propagation through underwater optical turbulence.
Optical applications, such as imaging, communications, and sensing, can be severely limited by the effects of oceanic turbulence when the water is free of particulate matter. To study this phenomenon in a controlled environment, a Rayleigh B`enard tank, housed at the U.S. Naval Academy, was used to study heat driven convective turbulence in a systemic manner. A Gaussian laser beam was characterized though synchronized phase and intensity measurements obtained by a Shack-Hartmann wavefront sensor and high-speed camera, respectively. The beam’s instantaneous intensity and phase measurements were analyzed in space and time, and the synchronicity between the wavefront sensor and camera allows for the temporal statistics to be directly compared. Phase time series were analyzed to obtain an ensemble averaged power spectrum that was fit to a bounded Kolmogorov model. Wavelet analysis was leveraged to process the turbulence frequency rates at weak and moderate turbulence levels. Estimates for the turbulence turnover rates were obtained from the temporal statistics. Upon applying the same methods to the intensity time series, the statistics appeared subtly different compared to the phase statistics. It was shown within the wavefront frequency statistics that features changed on the time scale of seconds. However, intensity features changed on timescales of seconds to a tenth of a second.
Characterization of the optical turbulence of complex media is important to designing resilient free-space optical communication systems. Previous studies have used machine learning algorithms to characterize optical turbulence in the atmospheric environment, but we propose to extend this concept to the underwater medium. Our experimental design propagates a Gaussian beam ~1.25 meters through a Rayleigh-Bénard (RB) turbulence tank, which creates realistic optical turbulence that is fully controllable and repeatable. The intensity and phase distortions of the Gaussian beam after propagation will be collected and used to train a convolutional neural network (CNN), for the purpose of the underwater optical turbulence characterization. The CNN will be trained to classify turbulence levels based on both intensity and phase measurements in varied levels of optical turbulence.
We consider the design and generation of spatially partially coherent (SPC) beams carrying orbital angular momentum (OAM) propagating through complex random media. It has been theoretically shown that spatial coherence can be controlled through a prescribed linear superposition of Laguerre-Gaussian (LG) modes. Experimentally the SPC beams are obtained by randomly cycling the phase screens of the coherent modes, with each mode contributing a weight that is proportional to its eigenvalue in the coherent mode decomposition equation. The spectral degree of coherence, ξ , theoretically varies from 0 (fully coherent) to 1 (incoherent). Experimentally, it is suggested that we can reach the highest level of incoherence when the modes are combined where LG mode orders are of equal weights. Preliminary measurements indicate a reduced coherence corresponding to increasing ξ. Our experimental design imposes turbulence on the beam to examine the effects of its spatial partial coherence on the scintillation index (SI). It has been shown that benefits to communication system performance, specifically underwater, can be achieved through the control of spatial coherence properties of laser light propagation.
Optical propagation through turbulence remains a topic of active research and is critically important to the development of novel optical communication systems in both air and water. A widely used tool to study propagation through turbulence are laboratory tanks where optically active turbulence is generated through heating and cooling of the horizontal tank walls, akin to classic Rayleigh-Bénard convection. An important complement to the laboratory setup are numerical simulations that can supplement the sparser laboratory measurements through full fields of temperature and velocity. Such simulations can also provide phase screens for modeling of optical propagation through turbulence. We performed numerical simulations of different configurations of Rayleigh-Bénard turbulence tanks for comparison to other physical and numerical convective tanks. Results then provided the basis for optical modeling and the description of beam wander due to optical turbulence.
Numerical simulations of a Rayleigh-Bénard turbulent convective flow are examined to determine the optical and mechanical turbulence properties and resulting index of refraction and temperature structure function fields with the goal of understanding the propagation characteristics of a laser beam carrying orbital angular momentum. Beams carrying orbital angular momentum are a topic of interest for secure high data density free-space communications systems in both the atmosphere and underwater environment. The choice of Rayleigh-Bénard convection provides a highly controllable configuration for studying optical turbulence and once the flow reaches a steady state, it may be treated as homogeneous. With a well characterized turbulent state provided by the simulations, attention is focused on the mechanics of beam propagation through the turbulence. Simulations are performed using the open source computational fluid dynamics package OpenFoam, a finite volume solver, and an in-house developed code that uses spectral methods. In the case of each solver, the Boussinesq approximation is used to model buoyancy and both the Navier-Stokes equations and the thermal energy equation are simultaneously solved. The outcome from the two computational schemes will be cross compared for result fidelity, spatial resolution, and computation time. The initial effort will examine air as the working medium in a domain with dimensions of 0.5 m on a side and a height of 0.1 m.
Communication in maritime environments presents unique challenges often requiring the secure transfer of information over long distances in a complex dynamic environment. Here a system for generating orbital angular momentum (OAM) beams, multiplexing, transmitting, and demultiplexing using a convolutional neural network (CNN) is presented. A single input from a 1550 nm seed laser is amplified, split into four separate beams that are directed and modulated by four switches, and the resulting beams directed into phase plates to create beams carrying OAM. These four beams constitute the individual channels. The beams are passed through several optical elements, coherently combined, and transmitted to a receiver at a range of 12 m. The resulting OAM beam spatial patterns are captured using a high speed short-wave infrared detector, concurrently transmitted to a workstation for storage, and processed in real-time using a trained CNN. Results from short range and quiescent environmental state show a pattern detection accuracy of <99%.
In this work we present the spatio-temporal characteristics of the surface expressions generated by various species of reef fish in visible and thermal wavebands with the intention of understanding the structures formed during locomotion, station-keeping, and feeding in a large scale aquarium environment. Data collected focused on diurnal events when the majority of the fish were active and overlapped with the feeding cycle of the marine animals. Expressions generated by a sea turtle (1 m) down to smaller fish (0.3 m) were observed and recorded with the resulting surface thermal footprints varying from one meter to several centimeters respectively. Surface thermal wakes and boils were recorded as fish swarmed near the surface, breached the water, and struck at food particles floating on the surface. This collection of surface thermal features serves as a template for expected outcomes in a more complex unconfined environment such as a harbor or blue water.
Imaging through scattering media is a highly sought capability for military, industrial, and medical applications. Unfortunately, nearly all recent progress was achieved in microscopic light propagation and/or light propagation through thin or weak scatterers which is mostly pertinent to medical research field. Sensing at long ranges through extended scattering media, for example turbid water or dense fog, still represents significant challenge and the best results are demonstrated using conventional approaches of time- or range-gating. The imaging range of such systems is constrained by their ability to distinguish a few ballistic photons that reach the detector from the background, scattered, and ambient photons, as well as from detector noise. Holography can potentially enhance time-gating by taking advantage of extra signal filtering based on coherence properties of the ballistic photons as well as by employing coherent addition of multiple frames. In a holographic imaging scheme ballistic photons of the imaging pulse are reflected from a target and interfered with the reference pulse at the detector creating a hologram. Related approaches were demonstrated previously in one-way imaging through thin biological samples and other microscopic scale scatterers. In this work, we investigate performance of holographic imaging systems under conditions of extreme scattering (less than one signal photon per pixel signal), demonstrate advantages of coherent addition of images recovered from holograms, and discuss image quality dependence on the ratio of the signal and reference beam power.
The turbulent effect from the Earth’s atmosphere degrades the performance of an optical imaging system. Many studies have been conducted in the study of beam propagation in a turbulent medium. Horizontal beam propagation and correction presents many challenges when compared to vertical due to the far harsher turbulent conditions and increased complexity it induces. We investigate the collection of beam propagation data, analysis, and use for building a mathematical model of the horizontal turbulent path and the plans for an adaptive optical system to use this information to correct for horizontal path atmospheric turbulence.
An automated approach for detecting the presence of watercraft in a maritime environment characterized by regions of land, sea, and sky, as well as multiple targets and both water- and land-based clutter, is described. The detector correlates a wavelet model of previously acquired images with those obtained from newly acquired scenes. The resulting detection statistic outperforms two other detectors in terms of probability of detection for a given (low) false alarm rate. It is also shown how the detection statistics associated with different wavelet models can be combined in a way that offers still further improvements in performance. The approach is demonstrated to be effective in finding watercraft in previously collected short-wave infrared imagery.
The turbulent effects from the Earth’s atmosphere degrade the performance of any optical
system within it. There have been numerous studies in the effects of atmospheric turbulence
on an imaging system that is pointed vertically to the sky looking at distant objects and the
seeing conditions associated with it. We investigate the calculation of the seeing conditions
with an imaging system pointed horizontally in terrestrial and maritime environments. We
have acquired video data of different horizontal paths in the infrared wavelengths and
performed data analysis that will be the basis of new characterizations and modeling of
horizontal path atmospheric turbulence.
Infrared imaging, in both laboratory and field settings, has become a vital tool in diagnosing near-surface thermalhydrodynamic
phenomena such as convective cells, accumulation of surfactant, and coherent turbulent structures. In this
presentation, we initially focus on a laboratory scale (0.01-1m) subsurface vertical turbulent water jet that serves as a
canonical flow. The jet has a slightly elevated temperature thus the warmer fluid serves as a passive marker. Infrared
image sequences of the surface thermal field were collected for various water jet flow rates and for both "clean" and
surfactant-contaminated surface conditions. Turbulent characteristics of the near-surface flow field were measured by
means of Digital Particle Image Velocimetry (DPIV), and these are used to examine the statistical nature of the coupled
thermal-hydrodynamic field. An analog of the laboratory jet is the discharge of power-plant cooling water through a
vertical pipe on the ocean floor. High-resolution airborne infrared imagery has recently been acquired of such a
discharge (from the Huntington Beach Generating Station, CA), and these data are compared with the laboratory results
in an attempt to understand striking spatial patterns discovered on the ocean surface.
KEYWORDS: Associative arrays, Data modeling, Image quality, Image compression, Super resolution, Denoising, Chemical species, Wavelets, Video, Short wave infrared radiation
We present several improvements to published algorithms for sparse image modeling with the goal of
improving processing of imagery of small watercraft in littoral environments. The first improvement
is to the K-SVD algorithm for training over-complete dictionaries, which are used in sparse
representations. It is shown that the training converges significantly faster by incorporating multiple
dictionary (i.e., codebook) update stages in each training iteration. The paper also provides several
useful and practical lessons learned from our experience with sparse representations. Results of three
applications of sparse representation are presented and compared to the state-of-the-art methods; image
compression, image denoising, and super-resolution.
The development, integration and testing of a compact system for wide-area persistence surveillance in dedicated
maritime environments is presented. The system is based around a large-format, 2560 x 512 pixel focal plane array,
high dynamic range (16 bit), mid-wave infrared (MWIR) imager operating at 30 Hz that is equipped with a 90°
horizontal field-of-view (HFOV) lens. The digitized image data is fed to a standard commercial-off-the-shelf (COTS)
workstation equipped with a graphical processing unit (GPU) that is used to perform image de-warping, non-uniformity
corrections, and algorithms for real-time object detection and tracking (NRL Harbor Tracking Software-NRLHaTS). Data is presented from several field experiments that illustrate the capabilities of the integrated system.
We present an approach for discriminating among dierent classes of imagery in a scene. Our intended application
is the detection of small watercraft in a littoral environment where both targets and land- and sea-based clutter
are present. The approach works by training dierent overcomplete dictionaries to model the dierent image
classes. The likelihood ratio obtained by applying each model to the unknown image is then used as the
discriminating test statistic. We rst demonstrate the approach on an illustrative test problem and then apply
the algorithm to short-wave infrared imagery with known targets.
We present a technique for small watercraft detection in a littoral environment characterized by multiple targets
and both land- and sea-based clutter. The detector correlates a tailored wavelet model trained from previous
imagery with newly acquired scenes. An optimization routine is used to learn a wavelet signal model that
improves the average probability of detection for a xed false alarm rate on an ensemble of training images.
The resulting wavelet is shown to improve detection on a previously unseen set of test images. Performance is
quantied with ROC curves.
This work offers a comparison of broadband shortwave infrared, defined as the spectral band from 0.9 to 1.7 μm, and hyperspectral shortwave infrared imagers in a marine environment under various daylight conditions. Both imagers are built around a Raytheon Vision Systems large format (1024×1280) indium-gallium-arsenide focal plane array with high dynamic range and low noise electronics. Sample imagery from a variety of objects and scenes indicates roughly the same visual performance between the two systems. However, we show that the more detailed spectral information provided by the hyperspectral system allows for object detection and discrimination. A vessel was equipped with panels coated with a variety of paints that possessed spectral differences in the 0.9 to 1.7 μm waveband. The vessel was imaged at various ranges, states of background clutter, and times of the day. Using a standard correlation receiver, it is demonstrated that image pixels containing the paint can be easily identified. During the exercise, it was also observed that both bow waves and near-field wakes from a wide variety of vessel traffic provide a spectral signature in the shortwave infrared waveband that could potentially be used for object tracking.
This paper presents a simple, fast, and robust method to estimate the blur kernel model, support size, and its
parameters directly from a blurry image. The edge profile method eliminates the need for searching the parameter
space. In addition, this edge profile method is highly local and can provide a measure of asymmetry and spatial
variation, which allows one to make an informed decision on whether to use a symmetric or asymmetric, spatially
varying or non-varying blur kernel over an image. Furthermore, the edge profile method is relatively robust to
image noise. We show how to utilize the concepts behind the statistical tools for fitting data distributions
to analytically obtain an estimate of the blur kernel that incorporates blur from all sources, including factors
inherent in the imaging system. Comparisons are presented of the deblurring results from this method to current
common practices for real-world (VNIR, SWIR, MWIR, and active IR) imagery. The effect of image noise on
this method is compared to the effect of noise on other methods.
KEYWORDS: Diffusion, Sensors, Image fusion, Data acquisition, Physics, Data fusion, Principal component analysis, Image registration, Signal processing, Analytical research
This work considers the problem of combining high dimensional data acquired from multiple sensors for the
purpose of detection and classification. The sampled data are viewed as a geometric object living in a highdimensional
space. Through an appropriate, distance preserving projection, those data are reduced to a lowdimensional
space. In this reduced space it is shown that different physics of the sampled phenomena reside on
different portions of the resulting "manifold" allowing for classification. Moreover, we show that data acquired
from multiple sources collected from the same underlying physical phenomenon can be readily combined in the
low-dimensional space i.e. fused. The process is demonstrated on maritime imagery collected from a visible-band
camera.
Infrared imaging has proven to be an invaluable tool for remotely detecting and tracking coupled near-surface thermalhydrodynamic
structures such as foam patches of breaking waves, Langmuir circulation and convective cells, thermal
impressions of water mass movement and pollutant effluxes. The ability to quantify such characteristics is vital to
determining the complex nature of heat transport, gas entrainment and momentum exchange across air-water interfaces.
These physical processes play an important role in determining global climate and their accurate description is necessary
for consistent weather modeling. In this presentation, we focus on a laboratory scale subsurface turbulent water jet that
serves as a canonical near surface event. The jet liquid has a slightly elevated temperature and is placed in close
proximity to the air-water interface of a quiescent water basin into which it flows. Infrared image sequences of the
surface thermal field were collected for various water jet flow rates and used to examine the detailed statistical nature of
the resulting coupled thermal-hydrodynamic field. We discuss the similarities of the spatial structure of the surface
thermal field in light of observations made with other sensing techniques, the relevant length and thermal scales present
and the order of the fluctuating surface thermal field using Karhunen-Loeve analysis.
We present image data and discuss naval sensing applications of SWIR and Hyperspectral SWIR imaging in littoral and
marine environments under various light conditions. These environments prove to be challenging for persistent
surveillance applications as light levels may vary over several orders of magnitude within and from scene to scene.
Additional difficulties include imaging over long water paths where marine haze and turbulence tend to degrade
radiation transmission, and discrimination of low contrast objects under low-light and night imaging. Image data
obtained from two separate passive sensor systems, both of which are built around an RVS large format (1280 x 1024)
InGaAs FPA with high dynamic range and low noise electronics, are presented. The SWIR camera imager is equipped
with a custom 300 mm focal length f/2 narrow field-of-view (6° diagonal) refractive telescope. The Hyperspectral
imager has a custom selectable 900/1800 mm focal length telescope with corresponding 1.55°/0.79° field-of-view and fnumbers
of 3/6 respectively. The sensor uses 1280 pixels in the spatial direction and a window of 192 are used for the
spectral and operates at a nominal frame rate of 120 Hz. To assess field performance of the SWIR/Hyperspectral
imagers, comparison is made to output from a scientific grade VNIR camera and two state-of-the-art low-light sensors.
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