A new approach to blind deconvolution of astronomical images

Vorontsov, S. V.; Jefferies, S. M.

doi:10.1088/1361-6420/aa5e16

Cited by 10 publications

(9 citation statements)

References 30 publications

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“…hold. Equations (24), (25) and the parameters L u , L k guarantee the monotonic descent of PALM and iPALM with respect to the objective or its surrogate [41,43]. The pseudo code for PALM and iPALM with this backtracking can be found in algorithm 1.…”

Section: Step Sizes and Backtrackingmentioning

confidence: 99%

“…To circumvent this issue, a common choice (e.g. [24][25][26]41]) is to normalize the kernel with respect to the 1-norm. Thus, together with the non-negativity, we assume that the kernel is in the unit simplex…”

Section: Kernel Regularizationmentioning

confidence: 99%

“…Thus, we take a blind approach and estimate the point spread function within our reconstruction and thereby learn this information from the recorded data. This approach is intrinsically related to blind deconvolution [22][23][24][25]. Figure 2 shows an example of blind versus non-blind image fusion applied to the real remote sensing data with unknown blurring kernel.…”

Section: Introductionmentioning

confidence: 99%

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Blind image fusion for hyperspectral imaging with the directional total variation

et al. 2018

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Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.AMS classification scheme numbers: 49M37, 65K10, 90C30, 90C90 PACS numbers: 42.30. Va, 42.68.Wt, 95.75.Pq, 95.75.Rs Figure 1. Three example data for image fusion in remote sensing. They each consist of a hyperspectral image (small image, only one channel shown) and an image of higher spatial resolution (large image). The goal is to create an image that has both high spatial and high spectral resolution.

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Section: Step Sizes and Backtrackingmentioning

confidence: 99%

Section: Kernel Regularizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Blind image fusion for hyperspectral imaging with the directional total variation

et al. 2018

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“…Image restoration is a technology that uses degraded images and some prior information to restore and reconstruct clear images, to improve image quality. At present, this technology has been widely used in many fields, such as medical imaging [12,13], astronomical imaging [14,15], remote sensing image [16,17], and so on. In this paper, the problem of image deblurring under impulse noise is considered.…”

Section: Introductionmentioning

confidence: 99%

A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

Xiang

Wang

et al. 2019

Electronics

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This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.

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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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A new approach to blind deconvolution of astronomical images

Cited by 10 publications

References 30 publications

Blind image fusion for hyperspectral imaging with the directional total variation

Blind image fusion for hyperspectral imaging with the directional total variation

A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

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

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