Blind deconvolution in optical diffusion tomography

Jefferies, S. M.; Schulze, Kathy J.; Matson, Charles L.; Stoltenberg, Kurt; Hege, E. K.

doi:10.1364/oe.10.000046

Cited by 20 publications

(5 citation statements)

References 11 publications

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“…To clarify one final point, note that a blind deconvolution method in DOT was recently studied by Jefferies et al 35 and by Matson. 29 Although our scheme and their method are both called "deconvolution," the meanings of deconvolution are very different in our usage and in theirs.…”

Section: Discussionmentioning

confidence: 99%

Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

Pei²,

Graber

et al. 2005

J. Biomed. Opt.

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We present the fourth in a series of studies devoted to the issue of improving image quality in diffuse optical tomography (DOT) by using a spatial deconvolution operation that seeks to compensate for the information-blurring property of first-order perturbation algorithms. Our earlier reports consider only static target media. Here we report spatial deconvolution applied to media with time-varying optical properties, as a model of tissue dynamics resulting from varying metabolic demand and modulation of the vascular bed. Issues under study include the influence of deconvolution on the accuracy of the recovered temporal and spatial information. The impact of noise is also explored, and techniques for ameliorating its information-degrading effects are examined. At low noise levels (i.e, < or = 5% of the time-varying signal amplitude), spatial deconvolution markedly improves the accuracy of recovered information. Temporal information is more seriously degraded by noise than is spatial information, and the impact of noise increases with the complexity of the time-varying signal. These effects, however, can be significantly reduced using simple noise suppression techniques (e.g., low-pass filtering). Results suggest that the deconvolution scheme should provide considerable enhancement of the quality of spatiotemporal information recovered from dynamic DOT techniques applied to tissue studies.

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Section: Discussionmentioning

confidence: 99%

Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

Pei²,

Graber

et al. 2005

J. Biomed. Opt.

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“…In particular, we have found that we can improve the resolution of images of objects embedded in highly scattering media (clouds), often letting us detect and identify an object that would have remained invisible. 11 This suggests the possibility that we can extend our imaging capability to unfavorable observing conditions such as viewing objects buried in or behind interstellar dust clouds.…”

Section: Image Restoration: Ao's Little Helpermentioning

confidence: 99%

Computing in optics - Computing and telescopes at the frontiers of optical astronomy

2003

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W e can trace our interest in recording and understanding the stars and planets back thousands of years. But it was not until the early 17th century with the telescope's invention that we recorded the first astronomical "images," hand drawings of our Moon's surfaces, the Sun, Venus, and Saturn, and Jupiter's satellites. Detailed drawings by Galileo Galilei and others surpassed anything previously seen and were pivotal in advancing the Copernican hypothesis that our Sun-rather than the Earth-was the center of all motion. This revolutionized our perspective of the heavens and planted a seed of curiosity about the existence of other Earth-like places. Early astronomers quickly realized that bigger is better when it came to telescopes. Larger tele-42 COMPUTING IN SCIENCE & ENGINEERING COMPUTING AND TELESCOPES AT THE FRONTIERS OF OPTICAL ASTRONOMY Advances in computing power are improving astronomical imaging by enabling adaptive-optics technology to mitigate atmospheric and other physical variables. Present-day 3.5-to 10-meter telescopes fitted with adaptive optics see farther and with higher resolution than the Hubble Space Telescope. The continuous evolution of ground-based telescope capabilities could make them suitable for applications previously envisioned only for space-based systems.

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“…Deconvolution is a ubiquitous data processing method that arises in a variety of applications, e.g., biomedical imaging (Jefferies et al 2002), astronomy (Prato et al 2012;Shearer 2013), remote sensing (Dong et al 2011), and video and photo enhancement (Fergus et al 2006). Formally, this problem amounts to recovering a signal x from blurred and corrupted observations y:…”

Section: Introductionmentioning

confidence: 99%

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Gonzalez

Delouille

Jacques

2016

J. Space Weather Space Clim.

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Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.

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Blind deconvolution in optical diffusion tomography

Cited by 20 publications

References 11 publications

Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

Computing in optics - Computing and telescopes at the frontiers of optical astronomy

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

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