Image Restoration of Io by Shift-and-Add Method and Deconvolution

Kuwamura, Susumu; Tsumuraya, Fumiaki; Miura, Noriaki; Baba, Naoshi

doi:10.1086/529550

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…The quality of astronomical images are seriously limited by different effects such as: photon-electronic noises from the detector, point spread functions of the imaging system and sky background noises. To further improve the quality of astronomical images, different image restoration methods are proposed for images of different astronomical targets observed in different wavelengths (Narayan & Nityananda 1986;Starck et al 1995;Bertero & Boccacci 2000;Esch et al 2004;Kuwamura et al 2008;La Camera et al 2012;Jia et al 2014;Xu et al 2020;Fétick et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Jia

Ning

Sun

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data–driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data–driven image restoration method based on generative adversarial networks with option–driven learning. Our method uses several high resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.

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Section: Introductionmentioning

confidence: 99%

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Jia

Ning

Sun

et al. 2020

Monthly Notices of the Royal Astronomical Society

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“…The raw contact images are then captured using the cellphone camera for each angular position of the fiber-optic array, creating a series of transmission images (e.g., 10-40 frames) for the same sample. These multi-frame images are digitally merged using a shift-and-add algorithm [43][44][45][46][47][48] implemented on a custom-developed Android application (see Fig. 2) running on the smart-phone, creating the final microscopic image of the sample that can be visualized and digitally zoomed in through the screen of the phone.…”

Section: Introductionmentioning

confidence: 99%

Smart-phone based computational microscopy using multi-frame contact imaging on a fiber-optic array

et al. 2013

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We demonstrate a cellphone based contact microscopy platform, termed Contact Scope, which can image highly dense or connected samples in transmission mode. Weighing approximately 76 grams, this portable and compact microscope is installed on the existing camera unit of a cellphone using an opto-mechanical add-on, where planar samples of interest are placed in contact with the top facet of a tapered fiber-optic array. This glass-based tapered fiber array has ∼9 fold higher density of fiber optic cables on its top facet compared to the bottom one and is illuminated by an incoherent light source, e.g., a simple light-emitting-diode (LED). The transmitted light pattern through the object is then sampled by this array of fiber optic cables, delivering a transmission image of the sample onto the other side of the taper, with ∼3× magnification in each direction. This magnified image of the object, located at the bottom facet of the fiber array, is then projected onto the CMOS image sensor of the cellphone using two lenses. While keeping the sample and the cellphone camera at a fixed position, the fiber-optic array is then manually rotated with discrete angular increments of e.g., 1-2 degrees. At each angular position of the fiber-optic array, contact images are captured using the cellphone camera, creating a sequence of transmission images for the same sample. These multi-frame images are digitally fused together based on a shift-and-add algorithm through a custom-developed Android application running on the smart-phone, providing the final microscopic image of the sample, visualized through the screen of the phone. This final computation step improves the resolution and also gets rid of spatial artefacts that arise due to non-uniform sampling of the transmission intensity at the fiber optic array surface. We validated the performance of this cellphone based Contact Scope by imaging resolution test charts and blood smears.

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Deconvolution of background-subtracted shift-and-add image by a modeled point-spread-function

Kuwamura

Tsumuraya

Sakamoto

et al. 2009

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Image Restoration of Io by Shift-and-Add Method and Deconvolution

Cited by 9 publications

References 32 publications

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Data-driven image restoration with option-driven learning for big and small astronomical image data sets

Smart-phone based computational microscopy using multi-frame contact imaging on a fiber-optic array

Deconvolution of background-subtracted shift-and-add image by a modeled point-spread-function

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