The effect of free parameter estimates on the reconstruction of turbulence corrupted images using the bispectrum

Bos, Jeremy P.; Roggeman, Michael C.

doi:10.1117/12.893859

Cited by 1 publication

(2 citation statements)

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“…where (12) shows that the parametric atmospheric PSF, h α ðrÞ, is the SE PSF convolved by a Gaussian motion blur impulse response. When α ¼ 0 (or equivalently β ¼ 1), the Gaussian motion blur standard deviation is the theoretical tilt standard deviation in Eq.…”

Section: Optical Transfer Functionsmentioning

confidence: 99%

“…One important class of turbulence mitigation algorithms is bispectral speckle imaging. [6][7][8][9][10][11][12][13][14][15][16] This method seeks to recover the ideal image in the Fourier domain, by estimating the magnitude and phase spectrum separately. The magnitude spectrum is obtained with an inverse filter, or pseudoinverse filter, based on the LE optical transfer function (OTF).…”

Section: Introductionmentioning

confidence: 99%

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Block matching and Wiener filtering approach to optical turbulence mitigation and its application to simulated and real imagery with quantitative error analysis

Hardie

Rucci

Dapore³

et al. 2017

Opt. Eng.

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We present a block-matching and Wiener filtering approach to atmospheric turbulence mitigation for long-range imaging of extended scenes. We evaluate the proposed method, along with some benchmark methods, using simulated and real-image sequences. The simulated data are generated with a simulation tool developed by one of the authors. These data provide objective truth and allow for quantitative error analysis. The proposed turbulence mitigation method takes a sequence of short-exposure frames of a static scene and outputs a single restored image. A block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames are then averaged, and the average image is processed with a Wiener filter to provide deconvolution. An important aspect of the proposed method lies in how we model the degradation point spread function (PSF) for the purposes of Wiener filtering. We use a parametric model that takes into account the level of geometric correction achieved during image registration. This is unlike any method we are aware of in the literature. By matching the PSF to the level of registration in this way, the Wiener filter is able to fully exploit the reduced blurring achieved by registration. We also describe a method for estimating the atmospheric coherence diameter (or Fried parameter) from the estimated motion vectors. We provide a detailed performance analysis that illustrates how the key tuning parameters impact system performance. The proposed method is relatively simple computationally, yet it has excellent performance in comparison with state-of-the-art benchmark methods in our study.

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Section: Optical Transfer Functionsmentioning

confidence: 99%