Foundations of Image Science

Barrett, Harrison H.

doi:10.1117/1.1905634

Cited by 191 publications

(484 citation statements)

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“…We assume that the CSF-ideal observer knew the noise statistics as well as the current letter contrast. The maximum likelihood decision is easiest to describe by prewhitening the stimulus (Barrett & Myers, 2004, p. 839; Chao, Tai, Dymek, & Yu, 1980; Cook & Bernfeld, 1967). That is, we compute the whitened stimulus

S^{'} (f_{x}, f_{y}) = S (f_{x}, f_{y}) / \sqrt{N (f_{x}, f_{y})}

for which the noise for each Fourier component is now a standard normal, and determine the maximum likelihood prewhitened letter

T_{i}^{'} (f_{x}, f_{y}) = T_{i} (f_{x}, f_{y}) / \sqrt{N (f_{x}, f_{y})}

.…”

Section: Methodsmentioning

confidence: 99%

Scale dependence and channel switching in letter identification

Oruç¹,

Landy²

2009

Journal of Vision

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Letters are broad-band visual stimuli with information useful for discrimination over a wide range of spatial frequencies. Yet, recent evidence suggests that observers use only a single, fixed spatial-frequency channel to identify letters, and that the scale of that channel, in units of letter size, is determined by the size of the letter (scale dependence). We report two letter-identification experiments using critical-band masking. With sufficiently high-amplitude, low- or high-pass masking noise, observers switched to a different range of spatial frequencies for the task. Thus, letter channels are not fixed for a given letter size. When an additional white-noise masker was added to the stimulus to flatten the contrast-sensitivity function, the letter channel used by the observer still depended on letter size, further supporting the hypothesis that letter identification is scale dependent.

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S^{'} (f_{x}, f_{y}) = S (f_{x}, f_{y}) / \sqrt{N (f_{x}, f_{y})}

for which the noise for each Fourier component is now a standard normal, and determine the maximum likelihood prewhitened letter

T_{i}^{'} (f_{x}, f_{y}) = T_{i} (f_{x}, f_{y}) / \sqrt{N (f_{x}, f_{y})}

.…”

Section: Methodsmentioning

confidence: 99%

Scale dependence and channel switching in letter identification

Oruç¹,

Landy²

2009

Journal of Vision

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“…Consider that the ultrasonic transducers collect data at Q locations that are specified by the index q = 0,…, Q − 1 and K temporal samples, specified by the index k = 0,…, K − 1, are acquired at each location with a sampling interval ΔT . A continuous-to-discrete (C-D) imaging model (Barrett & Myers, 2004; Wang & Anastasio, 2011) for OAT can be generally expressed as (Wang et al, 2011a):

{{[boldu]}_{q K + k} = u_{q} (t) ∣}_{t = k Δ T} = {h^{e} (t) *_{t} \frac{1}{Ω_{q}} \int_{Ω_{q}} d^{2} r^{'} p ({boldr}^{'}, t) ∣}_{t = k Δ T},

where u q ( t ) is the pre-sampled electric voltage signal corresponding to location index q , the surface integral is over the detecting area of the q -th transducer denoted by Ο q , and h e ( t ) denotes the acousto-electric impulse response (EIR) of transducers. The QK × 1 vector u denotes a lexiographically ordered version of the sampled data.…”

Section: Background: Imaging Models and Reconstruction Algorithms mentioning

confidence: 99%

Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

et al. 2012

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Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response, and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely, a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications.

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“…Since it is known that the nonnegative minimizers of the generalized KL divergence consist of a set of bright spots over a black background, the so-called night-sky solutions42, the algorithm can not be pushed to convergence and early stopping of the iterations is required for obtaining a sort of “regularization” effect. Recently a few stopping criteria have been proposed434438, but their utility in practice has still to be tested.…”

Section: Methodsmentioning

confidence: 99%

Towards real-time image deconvolution: application to confocal and STED microscopy

Zanella

Zanghirati

Cavicchioli

et al. 2013

Sci Rep

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Although deconvolution can improve the quality of any type of microscope, the high computational time required has so far limited its massive spreading. Here we demonstrate the ability of the scaled-gradient-projection (SGP) method to provide accelerated versions of the most used algorithms in microscopy. To achieve further increases in efficiency, we also consider implementations on graphic processing units (GPUs). We test the proposed algorithms both on synthetic and real data of confocal and STED microscopy. Combining the SGP method with the GPU implementation we achieve a speed-up factor from about a factor 25 to 690 (with respect the conventional algorithm). The excellent results obtained on STED microscopy images demonstrate the synergy between super-resolution techniques and image-deconvolution. Further, the real-time processing allows conserving one of the most important property of STED microscopy, i.e the ability to provide fast sub-diffraction resolution recordings.

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Foundations of Image Science

Cited by 191 publications

References 0 publications

Scale dependence and channel switching in letter identification

Scale dependence and channel switching in letter identification

Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

Towards real-time image deconvolution: application to confocal and STED microscopy

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