Measurement of the point-spread function of a noisy imaging system

Claxton, Christopher David; Staunton, Richard C.

doi:10.1364/josaa.25.000159

Cited by 61 publications

(32 citation statements)

References 21 publications

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“…Transforming the kernel: from k to h Recovering the samples of the camera psf h amounts to evaluate the limit in Eq (11). Directly working with the digital sequences requires some care in how the successive convolutions are computed.…”

Section: Numerical Methods For Inter-image Kernel Estimationmentioning

confidence: 99%

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Subpixel Point Spread Function Estimation from Two Photographs at Different Distances

Delbracio

Almansa

Morel

et al. 2012

SIAM J. Imaging Sci.

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In most digital cameras, and even in high-end digital single lens reflex cameras, the acquired images are sampled at rates below the Nyquist critical rate, causing aliasing effects. This work introduces an algorithm for the subpixel estimation of the point spread function of a digital camera from aliased photographs. The numerical procedure simply uses two fronto-parallel photographs of any planar textured scene at different distances. The mathematical theory developed herein proves that the camera psf can be derived from these two images, under reasonable conditions. Mathematical proofs supplemented by experimental evidence show the well-posedness of the problem and the convergence of the proposed algorithm to the camera in-focus psf. An experimental comparison of the resulting psf estimates shows that the proposed algorithm reaches the accuracy levels of the best non-blind state-of-the-art methods.

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Section: Numerical Methods For Inter-image Kernel Estimationmentioning

confidence: 99%

“…Several patterns have been used for psf estimation, ranging from pin-hole, slanted-edge [18,30,38,11], or arc-step-edge patterns [20,19] to random noise images [12,21,2,3,7]. Until recently, even non-blind subpixel psf estimation methods reported in the literature led to ill-posed inverse problems.…”

Section: Introductionmentioning

confidence: 99%

Subpixel Point Spread Function Estimation from Two Photographs at Different Distances

Delbracio

Almansa

Morel

et al. 2012

SIAM J. Imaging Sci.

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“…An experimental PSF is a (3D) PSF obtained by (z-stack) imaging a point source, e.g. a bead [26][27][28][29]. We verify our approach using simulations in the presence of extraneous noise sources and give practical examples.…”

Section: Introductionmentioning

confidence: 93%

“…A 3D experimental image set is a set of pixelated images of an object acquired at different defocus levels [27,28], which are corrupted by extraneous noise sources, such as background and readout noise, during the measurement process [5]. In addition, due to the stochastic nature of light, the acquired images are also inherently stochastic [9,10].…”

Section: Experimental Image Sets and Experimental Psfsmentioning

confidence: 99%

Determination of localization accuracy based on experimentally acquired image sets: applications to single molecule microscopy

Tahmasbi¹,

Ober²

2015

Opt. Express

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Abstract:Fluorescence microscopy is a photon-limited imaging modality that allows the study of subcellular objects and processes with high specificity. The best possible accuracy (standard deviation) with which an object of interest can be localized when imaged using a fluorescence microscope is typically calculated using the Cramér-Rao lower bound, that is, the inverse of the Fisher information. However, the current approach for the calculation of the best possible localization accuracy relies on an analytical expression for the image of the object. This can pose practical challenges since it is often difficult to find appropriate analytical models for the images of general objects. In this study, we instead develop an approach that directly uses an experimentally collected image set to calculate the best possible localization accuracy for a general subcellular object. In this approach, we fit splines, i.e. smoothly connected piecewise polynomials, to the experimentally collected image set to provide a continuous model of the object, which can then be used for the calculation of the best possible localization accuracy. Due to its practical importance, we investigate in detail the application of the proposed approach in single molecule fluorescence microscopy. In this case, the object of interest is a point source and, therefore, the acquired image set pertains to an experimental point spread function. information-theoretic analysis of single-molecule data," IEEE Signal Proc. Mag. 32(1), 58-69 (2015).

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“…A 1-dimensional (1D) generalised Gaussian PSF, generated using a combination of the Pillbox and Gaussian models with an adjustable parameter p is proposed in [22]. When p = 2, it is equivalent to a Gaussian PSF, and when p → ∞ it is equivalent to the Pillbox PSF.…”

Section: B the Generalised Gaussian Nirmentioning

confidence: 99%

Rational-operator-based depth-from-defocus approach to scene reconstruction

Staunton

Tjahjadi

2013

J. Opt. Soc. Am. A

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Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:This paper was published in Journal of the Optical Society of America A: Optics, Image Science and Vision and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://dx.doi.org/10.1364/JOSAA.30.001787 . Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. AbstractThis paper presents a rational operator based approach to depth from defocus (DfD) for the reconstruction of 3-dimensional scenes from 2-dimensional images, which enables fast DfD computation that is independent of scene textures. Two variants of the approach, one using the Gaussian rational operators (ROs) that are based on the Gaussian point spread function (PSF), and the second based on the generalised Gaussian PSF are considered. A novel DfD correction method is also presented to further improve the performance of the approach. Experimental results are considered on real scenes and show that both approaches outperform existing ROs-based methods.

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Measurement of the point-spread function of a noisy imaging system

Cited by 61 publications

References 21 publications

Subpixel Point Spread Function Estimation from Two Photographs at Different Distances

Subpixel Point Spread Function Estimation from Two Photographs at Different Distances

Determination of localization accuracy based on experimentally acquired image sets: applications to single molecule microscopy

Rational-operator-based depth-from-defocus approach to scene reconstruction

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