&lt;title&gt;Application of multiframe iterative blind deconvolution for diverse astronomical imaging&lt;/title&gt;

Christou, Julian C.; Hege, E. K.; Jefferies, S. M.; Keller, Christoph U.

doi:10.1117/12.177275

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…A major question concerns the uniqueness of the solutions obtained from blind deconvolution. While Miura and Baba (1995) claim that their reconstructions are unique, Christou et al (1994) failed to find a converging algorithm for solar images.…”

Section: Deconvolutionmentioning

confidence: 99%

12 Solar radiation & structure

1997

Trans. Int. Astron. Union

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Spatial structures in the solar photosphere are likely to be seen down to scales of the order of the photon mean free path, which is about 70 km in the lower photosphere. This scale corresponds to an angle of O.”1 at disk center. Structures associated with magnetic fields may be expected on even smaller scales. Existing solar telescopes typically have diameters of slightly less than one meter. Hence, even in the visible part of the spectrum, the scales of solar structures extend out to the diffraction limit of current solar telescopes. Therefore, the achievable spatial resolution is limited by turbulence in the Earth’s atmosphere (seeing). This has led to the development of various techniques to overcome this resolution limit and achieve diffraction-limited resolution. This report covers selected highlights and recent work done in the context of high-resolution techniques published in the period from July 1, 1993 to June 30, 1996. Due to the lack of space the report remains necessarily incomplete, and I apologize to all the authors of important contributions that are not cited here. This review does not cover space and balloon-borne instruments that try to achieve high spatial resolution by observing from above the Earth’s atmosphere. Recent work on ground-based high-resolution techniques has been collected in the proceedings of the 13th Sacramento Peak Summer Workshop on Real Time and Post Facto Solar Image Correction (Radick 1993).

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Section: Deconvolutionmentioning

confidence: 99%

12 Solar radiation & structure

1997

Trans. Int. Astron. Union

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“…The AO images in a real system are usually represented by the convolution of an ideal image with a point spread function (PSF) [ 3 ]. The AO image is also contaminated by other noises, such as read-out, photon counting, multiplicative, and compression noise [ 4 , 5 ]. In most practical applications, however, finding the real PSF is impossible and an estimation must be carried out.…”

Section: Introductionmentioning

confidence: 99%

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

Sun

Yang

et al. 2017

Sensors

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An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

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“…32 Consequently, multiple-frame deconvolution should result in systematically lower error bounds with more reliable results than when individual image frames are deconvolved separately or when multiple frames are merged into an averaged "shift-and-added" image (i.e., an image generated by averaging the image frames after appropriate pixel shifts are made to maximize image correlation) and then deconvolved. 2,11,[33][34][35][36] The extension to multi-frame deconvolution is straightforward. For multiple-image observations, Eq.…”

Section: Extension To Multiple-frame Datamentioning

confidence: 99%

“…3(B)] with a contrast enhancement of about 50%. Average contrast improvement was computed by multiple (N ≥ 6) comparisons of average intensities over an area of 3 × 3 pixels within a region of interest (I ROI ) versus over an adjacent background region (I background ) (separated by at least 4 pixels, the FWHM of the PSF): (35) Using the definition (36) we see signal-to-noise improvements of 6.2, 4.2, and 2.4 dB for the deconvolution results of SNR = 0, 10, and 20 dB images, respectively. Figure 4 shows the deconvolution results for the SNR = 20 dB image of Fig.…”

Section: Validation and Application To Mono-frame Datamentioning

confidence: 99%

AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data

et al. 2007

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We describe an adaptive image deconvolution algorithm (AIDA) for myopic deconvolution of multi-frame and three-dimensional data acquired through astronomical and microscopic imaging. AIDA is a reimplementation and extension of the MISTRAL method developed by Mugnier and co-workers and shown to yield object reconstructions with excellent edge preservation and photometric precision [J. Opt. Soc. Am. A 21, 1841 (2004)]. Written in Numerical Python with calls to a robust constrained conjugate gradient method, AIDA has significantly improved run times over the original MISTRAL implementation. Included in AIDA is a scheme to automatically balance maximum-likelihood estimation and object regularization, which significantly decreases the amount of time and effort needed to generate satisfactory reconstructions. We validated AIDA using synthetic data spanning a broad range of signal-to-noise ratios and image types and demonstrated the algorithm to be effective for experimental data from adaptive optics-equipped telescope systems and wide-field microscopy.

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<title>Application of multiframe iterative blind deconvolution for diverse astronomical imaging</title>

Cited by 11 publications

References 6 publications

12 Solar radiation & structure

12 Solar radiation & structure

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data

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