Real Time Deconvolution of Adaptive Optics Ground Based Telescope Imagery

Sanders, Toby; Hedges, R. E. M.; Schulz, Timothy J.; Abijaoude, Melena; Peters, John F.; Steinbock, Michael J.; Arreola, Anastacio; Holmes, Timothy

doi:10.1007/s40295-021-00285-w

Cited by 3 publications

(7 citation statements)

References 22 publications

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“…The extension of these operators to 2D images is done naturally by taking differences in both the vertical and horizontal dimensions, and mathematically this can be handled by taking appropriate Kronecker products (see [14] for details). For deconvolution problems, there is a major computational advantage to writing the operators appearing in (11) as circulant, making these operators convolutional operators. Hence, when T is a rth order finite difference operator, then it is also diagonalized by the Fourier transform given by (see [13,14] for details)…”

Section: Regularization Operatorsmentioning

confidence: 99%

“…The convergence of these algorithms could likely be improved with further refinement. One could, for example, implement an accelerated fixed point method [7,17], as was done for the fixed points in [11]. Another alternative would be to use additional variable p values for the exponent, based on some simple conditions.…”

Section: Testing Optimization Parametersmentioning

confidence: 99%

“…Other priors about the PSF may be contained in the imaging acquisition domain. For example, in telescope imaging and light microscopy, a parametric model for the PSF is designed based on the model of the lense aperture [6,8,11]. Some parameters in the model for the PSF are left as free variables, and an alternating optimization approach is used again by alternatively optimizing over the image and PSF.…”

Section: Introductionmentioning

confidence: 99%

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Fixed Point Iterations for SURE-based PSF Estimation for Image Deconvolution

Sanders¹

2022

Preprint

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Stein's unbiased risk estimator (SURE) has been shown to be an effective metric for determining optimal parameters for many applications. The topic of this article is focused on the use of SURE for determining parameters for blind deconvolution. The parameters include those that define the shape of the point spread function (PSF), as well as regularization parameters in the deconvolution formulas. Within this context, the optimal parameters are typically determined via a brute for search over the feasible parameter space. When multiple parameters are involved, this parameter search is prohibitively costly due to the curse of dimensionality. In this work, novel fixed point iterations are proposed for optimizing these parameters, which allows for rapid estimation of a relatively large number of parameters. We demonstrate that with some mild tuning of the optimization parameters, these fixed point methods typically converge to the ideal PSF parameters in relatively few iterations, e.g. 50-100, with each iteration requiring very low computational cost.

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Section: Regularization Operatorsmentioning

confidence: 99%

Section: Testing Optimization Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fixed Point Iterations for SURE-based PSF Estimation for Image Deconvolution

Sanders¹

2022

Preprint

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“…The image is of the well known Seasat satellite and is shown in the top left of Figure 5. In order to deconvolve this type of imagery, traditionally a blind deconvolution algorithm is implemented which takes into account the optics of the telescope [5,6]. Hence this approach provides us with a baseline comparison.…”

Section: Real Datamentioning

confidence: 99%

“…These models are usually non-convex, but pragmatic estimates can be obtained by alternating minimization over estimates of h 0 and u. Other priors about the PSF may be contained in the imaging acquisition domain, for example, in telescope imaging and light microscopy [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Blind Deconvolution Using the Sure-Blur Criterion and Linear PSF Expansions

Sanders

2022

2022 IEEE International Conference on Image Processing (ICIP)

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The article investigates a new approach to blind deconvolution, which approximates the underlying point spread function (PSF) as a linear combination of a series of PSFs. The coefficients in the linear expansion are the free parameters that we solve for to estimate the true PSF. Minimization of Stein's unbiased risk estimator (SURE) is used as the criterion to determine the coefficients. The numerical optimization procedure for estimating the coefficients is fast and relatively simple due to recent work [1]. The series of PSFs in the expansion are angled anisotropic Gaussian distributions generated with random parameters, and this approach is shown to work reasonably well in both simulated and real examples.

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Automatic Inhomogeneous Background Correction for Spatial Target Detection Image Based on Partition Processing

Jiang

Chen

et al. 2023

Photonics

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High-resolution imaging with wide field of view (FoV) ground-based telescopes is often affected by skylight background and noise due to the detector, resulting in an inhomogeneous background. In this paper, we propose an improved method for spatial image non-uniformity correction based on partition processing. First, an evaluation metric is introduced to evaluate the partition size and automatically iterate a suitable partition value for different scenarios based on the different operating conditions of the telescope. Then, we iteratively calculate the mean and variance in each partitioned region to extract the background of each partitioned region. Finally, after applying bilinear interpolation to the background extracted from each region, the inhomogeneous background is obtained and removed from the original image. The experiments on the simulated and real images show that the proposed method can effectively remove the inhomogeneous background of spatial images and meet the requirements of the real-time processing of high-resolution images under long exposure conditions.

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Real Time Deconvolution of Adaptive Optics Ground Based Telescope Imagery

Cited by 3 publications

References 22 publications

Fixed Point Iterations for SURE-based PSF Estimation for Image Deconvolution

Fixed Point Iterations for SURE-based PSF Estimation for Image Deconvolution

Blind Deconvolution Using the Sure-Blur Criterion and Linear PSF Expansions

Automatic Inhomogeneous Background Correction for Spatial Target Detection Image Based on Partition Processing

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