&lt;title&gt;Image restoration techniques for partially coherent 2-D ladar imaging systems&lt;/title&gt;

MacDonald, Adam; Cain, Stephen C.; Armstrong, Ernest E.

doi:10.1117/12.560278

Cited by 11 publications

(12 citation statements)

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“…2 This is due to the assumption that the phase at the target is random and independent from observation to observation in a manner consistent with the time-varying phase distribution produced by incoherent light. By capitalizing on this, the image plane model becomes a convolution of the geometric image of the source intensity and a point spread function ͑PSF͒.…”

Section: Image Plane Data Modelmentioning

confidence: 97%

Joint maximum likelihood estimator for pupil and image plane data

Phillips

Cain

2008

Opt. Eng

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Laser imaging through atmospheric turbulence is a challenging problem due primarily to turbulence near the aperture. Amplitude measurements of the return beam in the aperture plane are less sensitive to near-field turbulence, but contain limited information about the Fourier phase of the target image. We propose a method of using deconvolution of laser speckle degraded imagery, together with pupil plane techniques, to produce coherent image reconstructions with spatial resolution not limited by the atmospheric conditions. The technique is demonstrated using computer simulation. © 2008 Society of Photo-Optical Instrumentation Engineers.

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Section: Image Plane Data Modelmentioning

confidence: 97%

Joint maximum likelihood estimator for pupil and image plane data

Phillips

Cain

2008

Opt. Eng

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“…The method proposed in this work will rely on the convergence of the estimate of the noise power and the predicted variance of the collected data to cease iterations. Convergence of variance as a criteria to cease iterations has been used previously with success; 12,17 however, the novelty in this work is that we will also use the convergence of variance to identify the best model for the PSF parameterized by r 0 .…”

Section: Richardson-lucy Deconvolution Algorithmmentioning

confidence: 99%

“…He developed a blind technique that was iterative in nature like the RL algorithm, yet he introduced a priori information for images distorted by speckle noise following more of a negative binomial distribution. 12 MacDonald introduced a priori information for the distribution of r 0 in hopes of maximizing the likelihood at the appropriate level of seeing.…”

Section: Blind Estimate Of Seeing Via Map Techniquementioning

confidence: 99%

“…3, we will discuss various techniques for removing atmospheric blur maximum a priori (MAP) estimator developed for blind estimation of r 0 . 12 Additionally, a novel technique will be presented which improves upon the ability to blindly estimate r 0 . A performance comparison of the two blind algorithms on both fully and partially illuminated simulated scenes distorted by various noise distributions will be provided in Sec.…”

Section: Introductionmentioning

confidence: 99%

“…The problem we are attempting to solve was previously addressed by MacDonald. 2,12 This work by MacDonald focused on undoing the distorting effects of atmospheric turbulence in short exposure, partially coherent imaging of remote scenes at long slant ranges. Key contributions of the algorithm originally presented by MacDonald are as follows.…”

Section: Introductionmentioning

confidence: 99%

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Image deblurring and near-real-time atmospheric seeing estimation through the employment of convergence of variance

Neff¹,

MacManus²,

Cain³

et al. 2013

J. Appl. Remote Sens

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Abstract. A new image reconstruction algorithm is presented that will remove the effect of atmospheric turbulence on motion compensated frame average images. The primary focus of this research was to develop a blind deconvolution technique that could be employed in a tactical military environment where both time and computational power are limited. Additionally, this technique can be employed to measure atmospheric seeing conditions. In a blind deconvolution fashion, the algorithm simultaneously computes a high resolution image and an average model for the atmospheric blur parameterized by Fried's seeing parameter. The difference in this approach is that it does not assume a prior distribution for the seeing parameter, rather it assesses the convergence of the image's variance as the stopping criteria and identification of the proper seeing parameter from a range of candidate values. Experimental results show that the convergence of variance technique allows for estimation of the seeing parameter accurate to within 0.5 cm and often even better depending on the signal to noise ratio. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Weighted frame averaging for motion compensation of laser radar image data

MacDonald

2006 IEEE Aerospace Conference

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<title>Image restoration techniques for partially coherent 2-D ladar imaging systems</title>

Cited by 11 publications

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Joint maximum likelihood estimator for pupil and image plane data

Joint maximum likelihood estimator for pupil and image plane data

Image deblurring and near-real-time atmospheric seeing estimation through the employment of convergence of variance

Weighted frame averaging for motion compensation of laser radar image data

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