Increasing circular synthetic aperture sonar resolution via adapted wave atoms deconvolution.

Circular synthetic aperture sonar (CSAS) can provide fine image of interest region in three dimensions through the sonar movement along a circle at a fixed height. The severe computation complexity of the state-of-the-art imaging algorithms is of great trouble during the usage process of CSAS. According to the relationship between the azimuth and the peak-valued curve of the returns of CSAS, the isometric angle characteristic is revealed in the paper. A fast inverse algorithm of CSAS imaging employing the isometric angle characteristic is proposed combined with CLEAN algorithm. The numerical experiments show the effectiveness and the low computational complexity of the proposed algorithm.

Acoustic tomography can provide a two-dimensional cross-sectional view of real object through processing of many projections taken at different directions. This technique can insonifing the object and then recording the backscattered signal provides the projection information for a given aspect angle. Circular synthetic aperture sonar can combine object backscattered echoes in a circular trajectory during many aspect angles which to yield high-resolution image by tomographic. The aspect angles backscattered signals are obtained by experiment with benchmark moving in a circle. The two-dimensional shape of benchmark is reconstructed by applying Fourier reconstruction method, Backprojection method and Filtered backprojection method. The results show that both reconstruction images reveal the radially symmetric shape of the benchmark but the prominence of the acoustic highlights is different. The Fourier reconstruction method is preferred for acoustic image formation due to its efficiency and reconstruction accuracy.

The rich content of synthetic aperture sonar (SAS) data is typically used to generate imagery with resolution as high as theoretically possible. But when the ultimate purpose of the imagery is for detecting objects with sizes large compared to the resolution cell, exploiting the raw data in alternative ways can be more useful. We first show how multiple lower-resolution SAS images (i.e., “looks”) can be obtained in an efficient, principled manner by band-limiting the image wavenumber spectrum of a full-resolution SAS image. By combining these multiple looks (from different aspect and frequency bands), a despeckled image that enjoys greater (target) signal to (seabed) reverberation ratio can be produced. On a large set of real SAS data collected at sea, it is demonstrated how better target detection performance of underwater mines can be achieved by using the lower-resolution despeckled imagery rather than single-look, maximum-resolution imagery.

Squint multi-receiver synthetic aperture sonar (SAS) data is more challenging to process than data about its common side-looking counterpart because the geometry is complicated. While traditional multi-receiver algorithms mainly handle general side-looking cases, they do not work well for squint cases. Up to now, no multi-receiver algorithm for handling squint multi- receiver SAS data has been reported. This paper focuses on an imaging algorithm for the squint multi-receiver SAS. Firstly, the “non-stop-go-stop” signal model is established for squint multi- receiver SAS. Then a squint multi-receiver nonlinear chirp scaling algorithm (SMANLCSA) is proposed that is suited to process multi-receiver SAS data with small squint angle and SMANLCSA involves three key steps. The first step is to turn squint multi-receiver data into squint single-receiver data. The second step is a Doppler centroid frequency shift. The third step is range cell migration correction using nonlinear chirp scaling. Simulations have shown that SMANLCSA can handle squint multi-receiver data more accurately and effectively than the previous algorithm..

A computational camera uses a combination of optics and processing to produce images that cannot be captured with traditional cameras. Over the last decade, a range of computational cameras have been proposed, which use various optics designs to encode and use computation to decode useful visual information. What is often missing, however, is the quantitative analysis of the relation between camera design and the captured visual information, and little systematic work has been done to evaluate and optimize these computational camera designs. While the optics of computational cameras may be quite complicated, many of them can be effectively characterized by their point spread functions (PSFs): the intensity distribution on an image sensor as a response to a point light source in a scene. This thesis explores the techniques to characterize, evaluate and optimize computational cameras via PSF engineering for computer vision tasks, including image recovery, 3D reconstruction, and image refocusing. I first explore PSF engineering techniques to recover image details from blurry images. Image blur is a problem commonly seen in photography due to a number of reasons, including defocus, lens aberration, diffraction, atmospheric turbulence, object motion, and etc. Image blur is often formulated as a convolution of the latent blur-free image and a PSF, and deconvolution (or deblurring) techniques have to be used to recover image details from a blurry region. Here, I propose a comprehensive framework of PSF evaluation for the purpose of image deblurring, in which the effects of image noise, deblurring algorithm, and the structure of natural images are all taken into account. In defocus blur, the shape of defocus PSF is largely determined by the aperture pattern of the camera lens. By using an evaluation criterion derived from the comprehensive framework, I optimize the aperture pattern to preserve many more image details when defocus occurs. Both through simulations and experiments, I demonstrate the significant improvement gained by using optimized aperture patterns. While defocus blur causes a loss in image detail, it also encodes depth information of scenes in images. A typical depth from defocus (DFD) technique computes depth from two or more images that are captured with circular aperture lenses of different focus settings. Circular apertures produce circular defocus PSFs. In this thesis, I show that the use of a circular aperture severely restricts the accuracy of DFD, and propose a comprehensive framework of PSF evaluation for depth recovery. With this framework, we can derive a criterion for evaluating a pair of apertures with respect to the precision of depth recovery. This criterion is optimized using a genetic algorithm and gradient descent search to arrive at a pair of high resolution apertures. The two coded apertures are found to complement each other in the scene frequencies they preserve. With this property it becomes possible to not only recover depth with greater fidelity but also to obtain a high quality all-focused image from the two defocused images. While depth recovery can significantly benefit from optimized aperture patterns, its overall performance is rigidly limited by the lens aperture's physical size. To transcend this limitation, I propose a novel depth recovery technique using an optical diffuser - referred to as depth from diffusion (DFDiff). I show that DFDiff is analogous to conventional DFD, in which the scatter angle of the diffuser determines the system's effective aperture. High precision depth estimation can be achieved by choosing a proper diffuser and no longer requires the large lenses that DFD requires. Even a consumer camera with a low-end small lens can be used to do high-precision depth estimation when coupled with an optical diffuser. In my detailed analysis of the image formation properties of a DFDiff system, I show a number of examples demonstrating greater precision in depth estimation when using DFDiff. While the finite depth of field (DOF) of a lens camera leads to defocus blur, it also produces artistic visual experience. Many of today's displays are interactive in nature, which opens up a possibility for new kind of visual representations. Users could, for example, interactively refocus images to different depths, so that they can experience the artistic narrow DOF images while simultaneously making available the image detail for the entire image. To enable image refocusing, one typical approach is to capture the entire light field. But this method has the drawback of a significant sacrifice in spatial resolution due to the dimensionality gap: the captured information (light field) is 4D, while the required information (focal stack) is only 3D.In this thesis, I present an imaging system that directly captures focal stacks by a sweeping focal plane. First, I describe how to synchronize focus sweeping with image capturing so that the summed DOF of a focal stack efficiently covers the entire depth range. Then, I take a customized algorithm to enable a seamless refocusing experience, even in textureless regions or with moving objects. Prototype cameras are presented to capture real space-time focal stacks. There is also an interactive refocusing viewer available online at www.focalsweep.com.

The use of Multiple Input Multiple Output (MIMO) orthogonally coded waveforms sharing the same bandwidth, has the potential to increase area coverage rate in synthetic aperture sonar (SAS). The disadvantage is cross‐talk between the waveforms. In this letter, the benefits and limitations are listed for a best case use of MIMO in SAS. A scheme is developed to emulate a MIMO system with realistic cross‐talk that can be tested on non‐MIMO SAS data. The impact of cross‐talk is evaluated on simulated data and real data collected by a HISAS interfereometric SAS carried by a HUGIN autonomous underwater vehicle. The finding is that the negative impact of cross‐talk on SAS is significant.

Downward looking synthetic aperture sonar can detect and localize proud and buried objects, such as unexploded ordinance (UXO), in very shallow water environments. These sensors generate three-dimensional imagery from a two dimensional array that is a hybrid of a synthetic and real aperture sonar. Achieving high-resolution imaging with these sensors places stringent requirements on the sonar hardware. The acoustic signals generated by the sensor are wide bandwidth, low frequency, and high dynamic range. Receiver spacing and directivity also play an important role in image quality but create competing design constraints at low frequency. This work will present the results of a recent design study to develop receivers for a buried UXO imaging system. Achieving the stringent hardware requirements necessitated that the transducers, baffle, housing, and electronics were designed in concert. Finite element and one-dimensional models were used to evaluate several candidate designs based upon piezocomposite, spherical, and hemispherical transducers and identify tradeoffs with each. The analysis will focus on both simulated and experimental results of the receiver components. Results from the component level also informed system level models to quantify the point spread function and resolution improvement of the sensor.

The problems caused by random sway errors in the tow-path of a single hydrophone, synthetic aperture sonar (SAS) are well-known. If these horizontal displacement errors are left uncorrected and the tow path is assumed to be straight, they have a devastating effect on the quality of the reconstructed image. Although on-board navigation instruments can help measure the gross departures from the straight path, sub-wavelength residual sway errors still corrupt the imaging process. An early sway estimation technique in SAR (called the map-drift algorithm) was to divide the full aperture up into two equal, but non-overlapping sub-apertures. By cross-correlating the resultant two low-resolution images, an estimate of the equivalent phase error (that is the sway error divided by the wavelength and multiplied by /spl pi/) could be obtained for the second order coefficient of the polynomial describing the sway between the images. The two-image map-drift algorithm could be extended to smaller and smaller sub-apertures with the phase error estimated to higher and higher orders of the polynomial. The limit of the method is when the sub-aperture images are of such a low resolution that it is not possible to detect the differential sway between adjacent images. More recently SAS systems have a single transmitter +projector and an array of receiving hydrophones so that the echoes received from any one ping can be reconstructed into a low-resolution image centred on the rotational axis of the real array. The images from the adjacent pings are cross-correlated to determine how the sonar has moved between pings. The sub-aperture (cf. map-drift algorithm) is thus determined by the extent of the physical array. From the cross-correlation, the differential sway can be estimated from a series of displaced single ping images just as in the map-drift algorithm. By measuring the rotational displacement between the images we can also estimate the relative yaw between single ping images. Using simulated, distorted SAS data as input, the proposed DPIA algorithm shows promise in that it can estimate yaw with sufficient accuracy to restore distorted SAS images very close to their diffraction limit. Here we present "before and after" simulated images from an extant SAS and demonstrate the power (and also the limitations) of the proposed DPIA technique.

This paper describes a hybrid CPU/GPU implementation of the synthetic aperture sonar (SAS) processing algorithm developed under the Autonomous Naval Mine Counter Measures (ANMCM) programme at the NATO STO Centre for Maritime Research and Experimentation (CMRE). This new implementation allows processing of SAS data in real time onboard an autonomous underwater vehicle (AUV). This is a key component to enable the development of a higher level of autonomy, allowing the AUV to adapt the mission profile to environmental conditions and sonar performance in order to meet high level objectives. The software has been developed focusing on its embedded implementation on-board an AUV with a low-end NVIDIA GPU, but can also be used in post-processing taking advantage of a higher grade GPU. The first results obtained at sea demonstrated the capability to produce SAS images in real time.

Time-of-flight (TOF) and point spread function (PSF) modeling have been shown to improve PET reconstructions, but the impact on physicians in the clinical setting has not been thoroughly investigated. A lesion detection and localization study was performed using simulated lesions in real patient images. Four reconstruction schemes were considered: ordinary Poisson OSEM (OP) alone and combined with TOF, PSF, and TOF + PSF. The images were presented to physicians experienced in reading PET images, and the performance of each was quantified using localization receiver operating characteristic. Numerical observers (non-prewhitening and Hotelling) were used to identify optimal reconstruction parameters, and observer SNR was compared to the performance of the physicians. The numerical models showed good agreement with human performance, and best performance was achieved by both when using TOF + PSF. These findings suggest a large potential benefit of TOF + PSF for oncology PET studies, especially in the detection of small, low-intensity, focal disease in larger patients.

Linear systems theory combined with Fourier transform techniques provides a powerful means to characterize electro-optical systems. We present the description and analysis of a particular scanning IR system whose characteristic Point Spread Function (PSF) is space-variant. This peculiarity of the PSF arises from the scanning geometry; the IR sensor spins as it drops toward the ground. A system impulse response, or Point Spread Function (PSF), is derived from effects including diffraction, motion blur, chromatic aberration and detector area. Other effects may be taken into account by cascading PSF's. Once the system PSF is determined, the output of the system is the convolution of the input irradiance with the PSF. Alternatively, the output is the familiar inverse Fourier transform of the product of transforms. However, the space-variance of the PSF makes the latter option impossible and the former difficult. The convolution progresses in a spiral manner, with the PSF width changing continuously. The method is tested using Fortran programs and a digital image processor. The digital images produced show expected results, revealing a marked increase in resolution as the sensor nears the ground.

Synthetic aperture sonar (SAS) has been shown to be capable of focussing images of objects buried in sand, even when the sonar is operated below the critical grazing angle. Because the high resolution and clutter rejection provided by SAS may be an essential component of a successful buried mine detection system, we have investigated the SAS point response of a buried object. We simulate SAS data for a buried target, including the variations in signal intensity and phase along the synthetic aperture caused by transmission into the sediment. The sediment attenuation reduces the effective synthetic aperture, degrading the resolution. In addition, the mismatch of the SAS matched filter to this data causes a reduction in resolution and a shift in apparent position. We investigate these effects for transmission by the refracted path and by surface roughness scattering below the critical angle.

Synthetic Aperture Sonar (SAS) coherently processes the acoustic data acquired along a linear trajectory. The imaging process is in essence an inverse problem where the reflectivity of the seafloor has to be estimated. Several imaging algorithms have been proposed over the years including back-projection algorithms. One commonly assumed hypothesis, however, is that the antenna is motionless during transmission and reception. This hypothesis is known as the start-stop assumption. This paper questions the validity of this assumption, and proposes a full derivation of the SAS processing taking into account the vehicle motion by using the Lorentz transformation. The cell migration for the SAS system is computed and the validity limit of the stop-start assumption depending on the SAS system parameters is derived. Numerical examples of start-stop assumption violations are given and the Doppler cell migration correction on real SAS data are presented and discussed.

To realize direct image of faint object which is in the close proximity of very bright object could be technologically feasible by introducing asymmetric apodization. The point spread function (PSF) of an optical system with asymmetric apodization has been found to possess a so‐called good side and a bad side. The good‐side has decreased side lobes whereas the bad‐side shows increased side lobes. This has been observed when the aperture is divided into three parts viz. two semi‐circular edge rings and a central region with an apodizer. The asymmetry in the PSF has been found to increase with the width of the edge rings and is seen to decrease with the increased apodization of the central region.

When the degradation is mainly caused by the camera linear motion, or the out of focus lens of the imaging systems, the model of the degradation functions or known as the point spread function (PSF) can be established with the corresponding parameters. In the situations, the identification of the PSF can be converted to the identification of the parameter of the degradation functions. In order to increase the identification accuracy of the degradation function and the computational efficiency in the identification process, the low pass finite impulse response (FIR) filter which is named as the inverse adaptive amplitude spectrum filter (IAASF) proposed in this paper is applied to identify the PSF. First, the amplitude spectra of the degraded images are calculated with discrete fast Fourier transform (DFFT). Second, filtering the amplitude spectra with IAASF in order to accurately measure the distance of the characteristic curves on the filtered amplitude spectra image frames, i.e. measuring the distance of the central two spectra lines on amplitude spectra image frames for the linear motion blur, and measuring the diameter of the inner circle of the concentric circles on the amplitude spectra image frames for the defocus blur, thus, the parameter of the PSF can be determined. Therefore, the PSF can be obtained from the parametric model of degradation function. The original images have been restored by the identified degradation function and the degraded image with Wiener filtering. The experimental results show that the algorithm proposed in this paper has higher computational efficiency and the higher estimation accuracy of the degradation function while the degraded images have higher signal to noise ratio (SNR). The algorithm has certain reference significance for the restoration and identification of the turbulence degraded images yielded by the aero-optical effects.