Identifying instrumental optical aberrations directly from focal plane speckle data

There is high demand for deploying real-time or near real-time implementation of image reconstruction techniques for intelligence, surveillance, and reconnaissance (ISR) systems, as well as biomedical systems, and border applications. While many image restoration techniques have been implemented, their computational complexity makes them unsuitable for real-time systems. This work presents massively parallelized algorithms for two and three-dimensional image reconstruction of objects using deconvolution and bispectrum techniques. Speckle imaging (SI) techniques are more preferred for phase reconstruction because of its high restoration quality at the lower values of signal-to-noise ratio (SNR), especially for smaller targets, and its shift-invariant property which makes it insensitive to random motion of an object. This work proposes, a novel strategy for parallelization of recursive bispectrum algorithm. Furthermore, parallel bispectrum speckle imaging technique is used as an initial step for parallelized multi-frame blind deconvolution (MFBD) image restoration algorithm in order to speed-up the reconstruction process for anisoplanatic, long horizontal path imaging. The parallel hybrid Bispectrum- MFBD (B-MFBD) technique, through massive parallelization, provides significantly large improvement to both bispectrum SI and MFBD parts of the hybrid algorithm. The work also investigates phase diversity (PD) technique as the special case of MFBD algorithm, where the object is observed with a stack of intentionally defocused images. The known defocused information between phases is used to estimate the object and aberration function. The multi-plane PD was implemented for wide field fluorescence microscopy imaging of three-dimensional objects. The PD algorithm was subsequently used to generate 3D point cloud data for the image from its single view.

Unconventional computer aided imaging approaches, such as bi-spectrum or multi-frame blind deconvolution, have been used for some time to obtain high resolution images of space based objects from the ground. This looking up imaging scenario is characterized by r0 diameters usually much smaller than the telescope aperture, and isoplanatic angles on the same order as the object under observation. Air to ground imaging on the other hand, is often characterized by relatively large r0 values usually about the same size of the imaging aperture, but very small isoplanatic angles. In such a case, where the scene covers many isoplanatic patches, it has been shown that improvements in image quality can be obtained by dividing the field of view into sub-regions and applying processing algorithms to each sub-region. Because r0 is roughly the diameter of the aperture, the primary aberration over a subregion is tip-tilt. Block matching algorithms that correct only the local tip-tilt over the sub-regions have been developed to run in near real time. Correcting higher order local aberrations, using a blind deconvolution algorithm, can lead to improved results but requires increased computation. In this presentation we seek to compare air-to-ground imagery with different levels of processing; first global tip-tilt only correction; second local tip-tilt correction; and finally local tip-tilt plus high order correction. The goal is to determine if the increased detail obtained at each step is worth in increased processing complexity.

A novel second-order statistics-based blind deconvolution and equalizer technique is proposed. This technique makes use of a two-channel perfect reconstruction filter bank derived from a two-component polyphase decomposition of the transmission channel in order to make exact system identifications possible. The proposed blind deconvolution algorithm is superior to conventional algorithms in view of the simple structure and the uniqueness of the solution. In order to verify the effectiveness of this method, several computer simulations including a 256 QAM signal equalizer and a blurred image recovery have been shown.

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

This paper presents B-MFBD, a parallel hybrid of bispectrum speckle imaging (SI) and multi-frame blind deconvolution (MFBD) image reconstruction techniques for anisoplanatic, long horizontal path imaging. Our aim is to recover an enhanced version of a turbulence-corrupted image by massive parallelization of an SI and MFBD algorithms. The bispectrum SI technique is used in place of the multi-frame ensemble averaging to initialize the iterative parallel MFBD algorithm. B-MFBD technique, through massive parallelization, provides significantly large improvement in execution speed to both the bispectrum SI and MFBD parts of the hybrid algorithm. We report \(85\,\%\) improvement in processing time with respect to the sequential implementation of the same algorithm for a \(256 \times 256\), gray-scale image, with comparable improvement in image quality.

In many real-life situations, we need to reconstruct a blurred image in situations when no information about the blurring is available. This problem is known as the problem of blind deconvolution. There exist techniques for solving this problem, but these techniques are not rotation-invariant. Thus, the result of using this technique may change with rotation. So, if we rotate the image a little bit, the method, in general, leads to a different deconvolution result. Therefore, even when the original reconstruction is optimal, the reconstruction of a rotated image will be different and, thus, not optimal. To improve the quality of image decomposition, it is desirable to modify the current state-of-the art techniques by making them rotation-invariant. In this paper, we show how this can be done, and we show that this indeed improves the quality of blind deconvolution.

: The overall long-term goal for this project is to develop engineering tools that are useful to the Navy as it operates in uncertain, partially known, or unknown ocean environments. During the last year, this project has focused on further determining the utility of a fully-passive propagation-physics-based technique for blind deconvolution of array-recorded sounds from a remote source with emphasis on determining how sparse-array measurements might be used for this task. The long term goals of this project are: i) to determine the effectiveness of synthetic time reversal (STR) for the purposes of blind deconvolution in noisy unknown ocean sound channels, ii) to effectively apply STR to marine mammal sounds recorded in the ocean with vertical and/or horizontal arrays, and iii) to utilize the STR-estimated signals and ocean-sound-channel impulse responses to classify, localize, and/or track individual marine mammals (or other sound sources) of interest. Since early 2009 this project has focused on developing an acoustic-ray-based version of synthetic time reversal (STR), a fully-passive technique for recovering the original signal and the source-to-arrayelement impulse responses for a remote unknown sound source in an unknown underwater waveguide [1-4]. The specific objectives are to: a) determine STR performance as a function of the signal-tonoise, array size, and array element number using acoustic propagation simulations, b) verify these findings with simple airborne- or water-borne acoustic laboratory experiments involving multiple receivers and multiple ray paths, c) obtain and process underwater array recordings of remote-butcooperative sound sources, and d) obtain and process marine mammal vocalizations for the purposes of marine mammal localization, tracking, and identification. This research effort extends the prior modebased version of STR [1] to higher frequencies, smaller receiving arrays, sparse receiving arrays, and sound channels with modal dispersion

The correction of an adaptive optics (AO) system is always partially, a post-processing technique such as blind deconvolution can be used to improve the image quality. In this paper, we propose a blind deconvolution algorithm using Zernike-polynomial-based phase fitting to recover a high resolution and contrast image from the short-exposure AO images. The strategy is to parameterize the point spread function (PSF) with phase aberrations pixel by pixel and introduce a constraint to the phase estimations using phase fitting. The algorithm is robust and can lead to a restoration free of artifacts. We evaluate the performance of our proposed algorithm by a number of simulated experiments and it is well performed.

High-contrast imaging is a growing observational technique aimed at discovering and characterizing extrasolar planets. The Gemini Planet Imager (GPI) is designed to achieve contrast ratios of 10^-6 - 10^-7 and requires unprecedented wavefront correction and coronagraphic control of diffraction. G PI is a facility instrument now undergoing integration and testing and is scheduled for first light on the 8-m Gemini South telescope towards the end of 2012. In this paper, we focus on the wavefront sensing and correction aspects of the instrument. To measure the wavefront, GPI combines a Shack-Hartmann wavefront sensor and a high-accuracy infrared interferometric wavefront calibration system. The Shack-Hartmann wavefront sensor uses 1700 subapertures to precisely sample the wavefront at 1.5 kHz and features a spatial filter to prevent aliasing. The wavefront calibration system measures the slower temporal frequency errors as well as non-common path aberrations. The wavefront correction is performed using a two-stage adaptive optics system employing a 9x9 piezoelectric deformable mirror and a 43x43 actuators MEMS deformable mirror operating in a woofer-tweeter configuration. Finally, an image sharpening technique is used to further increase the contrast of the final image. In this paper, we describe the three wavefront sensing methods and how we combine their respective information to achieve the best possible contrast.

ABSTRACTMeasurements of the brightness and size of a mesospheric sodium guide star pumped by a laser launched through an adaptive optics (AO) system are reported from experiments conducted on 2003 July 22 at the Starfire Optical Range. The guide star was produced by a laser launched through a 0.5 m telescope with (closed loop) and without (open loop) higher order atmospheric compensation using an ∼60 actuator AO system and with various laser polarization states. A 3.5 m telescope (without AO) 50 m away from the launch site recorded the closed‐ and open‐loop images. Calibrated using standard stars, the maximum brightness observed for the 8.5 W of compensated pump laser power out of the top of the telescope for a 96% circularly polarized beam was V1 = 7.35 (840 photons s−1 cm−2 at the bottom of the atmosphere) for the open‐loop beam (where V1 is the V magnitude corrected for the transmission of the V filter at the 589 nm wavelength of the sodium D2 absorption line) and V1 = 7.4 (800 photons s−1 cm−2) for the closed‐loop beam. It appears that circular polarization enhances the brightness of the return by 75%. Open‐loop tests produced an integrated guide star that was 11% brighter than closed‐loop, but the guide star produced from a closed‐loop circularly polarized pump beam was about 50% of the width of either the open‐loop beam or the linearly polarized closed‐loop beam. At 0.″85 FWHM, or 38 cm at 92 km altitude, the guide star is suitable for many astronomical AO applications. The guide star spot sizes were measured from images reconstructed with multiframe blind deconvolution. Because of optical losses, only 8.5 W of laser power was transmitted out of the top of the telescope. Reducing this by atmospheric extinction, the “power through the mesospheric footprint,” or peak Gaussian intensity, was 3.9 mW cm−2 for the closed‐loop circularly polarized laser beam. This is about 60% of the saturation intensity Isat for the optically pumped atom, and 20% of the Isat for the thermally equilibrated atom.

We propose a multi-frame blind deconvolution method using an in-plane rotating sample optimized for X-ray microscopy, where the application of existing deconvolution methods is technically difficult. Untrained neural networks are employed as the reconstruction algorithm to enable robust reconstruction against stage motion errors caused by the in-plane rotation of samples. From demonstration experiments using full-field X-ray microscopy with advanced Kirkpatrick–Baez mirror optics at SPring-8, a spatial resolution of 34 nm (half period) was successfully achieved by removing the wavefront aberration and improving the apparent numerical aperture. This method can contribute to the cost-effective improvement of X-ray microscopes with imperfect lenses as well as the reconstruction of the phase information of samples and lenses.

Recent advances are presented for multiframe blind deconvolution (MFBD) of ground based telescope imagery for low-earth orbit objects. The iterative algorithm uses the maximum likelihood estimation optimization criterion. It is modeled from a previous well-known algorithm called the expectation-maximization (EM) algorithm. New renditions of the algorithm simplify the phase reconstruction, thereby reducing the complexity of the original EM algorithm. Examples are shown, with and without adaptive optics (AO). The system is being designed for on-the-fly streaming video operation.

A wavelet-based technique for blind deconvolution and denoising of ultrasound scans is introduced. The target application is near-field ultrasound imaging for non-destructive testing. Existing blind deconvolution techniques for ultrasound such as cepstrum-based methods and the work of Adam and Michailovich – based on discrete wavelet transform (DWT) shrinkage of the log-spectrum – estimate the pulse by exploiting the pulse log-spectrum smoothness relative to the material reflectivity function. In the proposed technique, the log-spectrum is localised with respect to time as the continuous wavelet transform (CWT) log-scalogram to deal with the non-stationarity of the near-field ultrasound signals in both the pulse estimation and deconvolution. The pulse is estimated in the wavelet domain via DWT shrinkage of the log-scalogram and is deconvolved by wavelet-domain Wiener filtering. Extensions of the proposed technique include: using separate CWT domains for estimation and deconvolution, as inspired by the WienerChop denoising method; and training the algorithm parameters on a subset of scans.

Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolution images can be reconstructed from several blurred, noisy and down sampled low resolution images using a computational process know as super resolution reconstruction. The problem of recovering a high resolution image from a sequence of low resolution compressed images is considered. In this paper, we propose lifting schemes for intentionally introducing down sampling of the high resolution image sequence before compression and then utilize super resolution techniques for generating a high resolution image at the decoder. Lifting wavelet transform has its advantages over the ordinary wavelet transform by way of reduction in memory required for its implementation. This is possible because lifting transform uses in-place computation. The lifting coefficients replace the image samples present in the respective memory locations. In our proposed approach the forward lifting is applied to the high resolution images which are compressed using Set Portioning in Hierarchical Trees (SPHIT), the compressed images are transmitted which results in low resolution images at the encoder, at the decoder super resolution techniques are applied; lifting scheme based fusion is performed, then decoded using DSPIHT and Inverse lifting is applied. In order to remove noise from the reconstructed image soft thresholding is performed and the blur is removed using blind deconvolution, and finally interpolated using our novel interpolation technique. We have performed both objective and subjective analysis of the reconstructed image, and the resultant image has better super resolution factor, and a higher ISNR and PSNR.

As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.