Restoration of Images Taken Through a Turbulent Medium

This thesis investigates the problem of how information contained in multiple, short exposure images of the same scene taken through (and distorted by) a turbulent medium (turbulent atmosphere or moving water surface) may be extracted and combined to produce a single image with superior quality and higher resolution. This problem is generally termed image restoration. It has many applications in fields as diverse as remote sensing, military intelligence, surveillance and recognition at a long distance, and other imaging problems which suffer from turbulent media, including e.g. the atmosphere and moving water surface. Wide-area/near-to-ground imaging (through atmosphere) and water imaging are the two main focuses of this thesis. The central technique used to solve these problems is speckle imaging, which is used to process a large number of images of the object with short exposure times such that the turbulent effect is frozen in each frame. A robust and efficient method using the bispectrum is developed to recover an almost diffraction-limited sharp image using the information contained in the captured short exposure images. Both the accuracy and the potential of these new algorithms have been investigated. Motivated by the lucky imaging technique which was used to select superior frames for astronomical imaging application, a new and more efficient technique is proposed. This technique is called lucky region, and it is aimed at selecting image regions with high quality as opposed to selecting a whole image as a lucky image. A new algorithm using bicoherence is proposed for lucky region selection. Its performance, as well as practical factors that may affect the performance, are investigated both theoretically and empirically. To further improve the quality of the recovered clean image after the speckle bispectrum processing, we also investigate blind deconvolution. One of the original contributions is to use natural image sparsity as a prior knowledge for the turbulence image restoration problem. A new algorithm is proposed and its performance is validated experimentally. The new methods are extended to the case of water imaging: restoration of images distorted by moving water waves. It is shown that this problem can be effectively solved by techniques developed in this thesis. Possible practical applications include various forms of ocean observation

Comparing Multiple Turbulence Restoration Algorithms Performance on Noisy Anisoplanatic Imagery

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a block matching method with restoration filter. These algorithms were chosen because they incorporate different approaches and processing techniques. The results quantitatively show how well the algorithms are able to restore the simulated degraded imagery

BATUD: Blind Atmospheric TUrbulence Deconvolution

A new blind image deconvolution technique is developed for atmospheric turbulence deblurring. The originality of the proposed approach relies on an actual physical model, known as the Fried kernel, that quantifies the impact of the atmospheric turbulence on the optical resolution of images. While the original expression of the Fried kernel can seem cumbersome at first sight, we show that it can be reparameterized in a much simpler form. This simple expression allows us to efficiently embed this kernel in the proposed Blind Atmospheric TUrbulence Deconvolution (BATUD) algorithm. BATUD is an iterative algorithm that alternately performs deconvolution and estimates the Fried kernel by jointly relying on a Gaussian Mixture Model prior of natural image patches and controlling for the square Euclidean norm of the Fried kernel. Numerical experiments show that our proposed blind deconvolution algorithm behaves well in different simulated turbulence scenarios, as well as on real images. Not only BATUD outperforms state-of-the-art approaches used in atmospheric turbulence deconvolution in terms of image quality metrics, but is also faster

Photon Counting EMCCDs: New Opportunities for High Time Resolution Astrophysics

Electron Multiplying CCDs (EMCCDs) are used much less often than they might be because of the challenges they offer camera designers more comfortable with the design of slow-scan detector systems. However they offer an entirely new range of opportunities in astrophysical instrumentation. This paper will show some of the exciting new results obtained with these remarkable devices and talk about their potential in other areas of astrophysical application. We will then describe how they may be operated to give the very best performance at the lowest possible light levels. We will show that clock induced charge may be reduced to negligible levels and that, with care, devices may be clocked at significantly higher speeds than usually achieved. As an example of the advantages offered by these detectors we will show how a multi-detector EMCCD curvature wavefront sensor will revolutionise the sensitivity of adaptive optics instruments and been able to deliver the highest resolution images ever taken in the visible or the near infrared.Comment: 9 pages, 5 Figures; SPIE vol 8453, 201

Adaptive Optics for Astronomy

Adaptive Optics is a prime example of how progress in observational astronomy can be driven by technological developments. At many observatories it is now considered to be part of a standard instrumentation suite, enabling ground-based telescopes to reach the diffraction limit and thus providing spatial resolution superior to that achievable from space with current or planned satellites. In this review we consider adaptive optics from the astrophysical perspective. We show that adaptive optics has led to important advances in our understanding of a multitude of astrophysical processes, and describe how the requirements from science applications are now driving the development of the next generation of novel adaptive optics techniques.Comment: to appear in ARA&A vol 50, 201

AOLI-- Adaptive Optics Lucky Imager: Diffraction Limited Imaging in the Visible on Large Ground-Based Telescopes

The highest resolution images ever taken in the visible were obtained by combining Lucky Imaging and low order adaptive optics. This paper describes a new instrument to be deployed on the WHT 4.2m and GTC 10.4 m telescopes on La Palma, with particular emphasis on the optical design and the expected system performance. A new design of low order wavefront sensor using photon counting CCD detectors and multi-plane curvature wavefront sensor will allow dramatically fainter reference stars to be used, allowing virtually full sky coverage with a natural guide star. This paper also describes a significant improvements in the efficiency of Lucky Imaging, important advances in wavefront reconstruction with curvature sensors and the results of simulations and sensitivity limits. With a 2 x 2 array of 1024 x 1024 photon counting EMCCDs, AOLI is likely to be the first of the new class of high sensitivity, near diffraction limited imaging systems giving higher resolution in the visible from the ground than hitherto been possible from space.Comment: SPIE vol 8446, 201