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

Li, Dongming; Sun, Changming; Yang, Jian; Liu, Huan; Peng, Jia-qi; Zhang, Lijuan

doi:10.3390/s17040785

Cited by 13 publications

(11 citation statements)

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“…In this section, we demonstrate the performance of our algorithm on simulated images and real adaptive optics images. First, the simulated data for different SNR are used to compare results of the RL-IBD method [23], the ML-EM method [24], the CPF-adaptive method [25], the RMF-MLE method [26], and our method. Second, the performance of our method is evaluated on adaptive optics images taken by a 1.2 m AO telescope from Yunnan Observatory, China.…”

Section: Resultsmentioning

confidence: 99%

“…The parameters are set the same as the 1.2 m AO telescope on Yunnan Observatory, China. The set of experiments compares the proposed method with the RL-IBD method [23], the ML-EM method [24], the CPF-adaptive method [25], and the RMF-MLE method [26]. The setup for the simulated data experiment was the following.…”

Section: A Simulated Data Experimentsmentioning

confidence: 99%

“…those of the RL-IBD method [23], the ML-EM method [24], the CPF-adaptive method [25], the RMF-MLE method [26], and the iteration number for the five methods is 150.…”

Section: A Simulated Data Experimentsmentioning

confidence: 99%

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Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

Qiu

Zhang

2020

IEEE Access

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In order to compensate for any failure on the use of point spread function (blur kernel) estimation and image estimation priors, we propose a novel regularization priors scheme with adapting the parameter for image restoration involving adaptive optics (AO) images. Our scheme uses a maximum a posteriori estimation with Gaussian statistics on the image and point spread function (blur kernel). An efficient regularization prior method associated with alternating minimization method is described to obtain the optimal solution recursively. Our method is applied to synthetic and real adaptive optics images. After applying our restoration method, satisfying results are obtained. Experimental results demonstrate that our proposed model and method performs better for restoring images in terms of both subjective results and objective assessments than the current state-of-the-art restoring methods. In addition, our proposed method can be a new way to promote their performances for AO image restoration.

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Section: Resultsmentioning

confidence: 99%

Section: A Simulated Data Experimentsmentioning

confidence: 99%

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Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

Qiu

Zhang

2020

IEEE Access

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“…Many MFBD algorithms and theoretical results have been developed; they used different a priori information in image restoration. Conventional MFBD algorithms usually assume that the observed images are corrupted by a single type of noise, either Poisson noise [1][2][3] or Gaussian noise [4,5]. Instead of adopting these strategies, we propose a novel multi-frame image restoration algorithm by adopting a mixed noise model (MFRAM); MFRAM can achieve a faster convergence, reduce noise more effectively and preserve more image details.…”

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confidence: 99%

Multi‐frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models

Yang

Jiang

2018

Electron. lett.

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This version is available at https://strathprints.strath.ac.uk/62591/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. This letter proposes a mixed noise model and uses the multi-frame blind deconvolution to restore the images of space objects under the Bayesian inference framework. To minimize the cost function, an algorithm based on iterative recursion was proposed. In addition, three limited bandwidth constraints of the point spread functions were imposed into the solution process to avoid converging to local minima. Experimental results show that the proposed algorithm can effectively restore the turbulence degraded images and alleviate the distortion caused by the noise.Introduction: Space object surveillance plays a fundamental and critical role in future space exploration. Images of space objects are usually acquired with ground-based telescopes. The image resolution, however, is limited due to the presence of the atmospheric turbulence (which causes the uneven distribution of the refractive index and leads to the wavefront distortion). This greatly deteriorates the quality and resolution of the images. A powerful approach called multi-frame blind deconvolution (MFBD) can significantly reduce the impact of atmospheric turbulence on an imaging system. MFBD can simultaneously estimate the unblurred object and the point spread functions (PSF) from a set of observed noise-inflicted images. The key step of applying MFBD is to accurately introduce a priori information in the restoration process. Many MFBD algorithms and theoretical results have been developed; they used different a priori information in image restoration. Conventional MFBD algorithms usually assume that the observed images are corrupted by a single type of noise, either Poisson noise [1][2][3] or Gaussian noise [4,5]. Instead of adopting these strategies, we propose a novel multi-frame image restoration algorithm by adopting a mixed noise model (MFRAM); MFRAM can...

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“…[Sch93] use multiframe techniques to either blind estimate the wavefront deformations or use them to refine a coarse estimate, which is called myopic algorithm [MRC+99]. Furthermore application specific priors are used such as the assumed Gaussian shape of the image from a star or a photon noise model [FMC+03] or Poisson noise [LSY+17] in intensity based MAP estimators. The same MAP approach with the incoherent imaging model is chosen in [MFC04] with myopic PSF estimation and an edge preserving prior.…”

Section: Image Deconvolutionmentioning

confidence: 99%

Restoration of Coherent Images

Zelenka¹

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In this thesis a series of novel algorithms for high quality restoration of coherent images is introduced. This task cannot be solved with established methods for the restoration of incoherent images. These algorithms focus on the correction of images in coherent imaging systems with a-priori known aberrations. The new wavefront correction algorithms achieve a significantly higher restoration quality than any previously known technique. The algorithms in this thesis are based on latest advances in optimization algorithms, particularly projections onto convex sets, proximal optimization and fractal self-similarity. Convergence and performance of the individual algorithms are analyzed in detail in various scenarios on real and simulated images. The evaluation also deals with the impact of noise on the restoration quality. Practical application of the new algorithms on microscopic images of diverse biological and human samples, as well as shadowgraph images of plankton acquired with a laboratory setup prove their efficiency. The new algorithms also have promising future applications in other areas, for example in adaptive optics and astronomy.

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

Cited by 13 publications

References 30 publications

Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics

Multi‐frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models

Restoration of Coherent Images

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