A model for measurement of noise in CCD digital-video cameras

Irie, Kenji; McKinnon, Alan E.; Unsworth, K.; Woodhead, Ian

doi:10.1088/0957-0233/19/4/045207

Cited by 55 publications

(32 citation statements)

References 17 publications

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“…During amplification, each pixel's signal S i is taken from the distribution described in Eq. (2). The new task is now to concurrently find the model parameters θ * and the flux of the image E * , which maximize the probability to observe the image S of signals.…”

Section: Localization With the Complete Nmlamentioning

confidence: 99%

“…The other parts of our method would remain unaffected. A different source of noise called dark signal is a Poisson distributed charge arising from imperfections in the production process of the chip [2]. Dark signal is pixel dependent [2] and its intensity pattern on the sensor can be measured.…”

Section: Perspectivementioning

confidence: 99%

“…In the commonly used cameras with a charge-coupled device (CCD), the signal is additionally corrupted by additive Gaussian readout noise. It originates from the circuitry used to amplify the analog signal and finally convert it into a digital signal [2]. Readout noise poses a severe problem when little light is available.…”

Section: Introductionmentioning

confidence: 99%

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A divide and conquer strategy for the maximum likelihood localization of low intensity objects

Krull¹,

Steinborn²,

Parton³

et al. 2014

Opt. Express

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Abstract:In cell biology and other fields the automatic accurate localization of sub-resolution objects in images is an important tool. The signal is often corrupted by multiple forms of noise, including excess noise resulting from the amplification by an electron multiplying charge-coupled device (EMCCD). Here we present our novel Nested Maximum Likelihood Algorithm (NMLA), which solves the problem of localizing multiple overlapping emitters in a setting affected by excess noise, by repeatedly solving the task of independent localization for single emitters in an excess noise-free system. NMLA dramatically improves scalability and robustness, when compared to a general purpose optimization technique. Our method was successfully applied for in vivo localization of fluorescent proteins.

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Section: Localization With the Complete Nmlamentioning

confidence: 99%

Section: Perspectivementioning

confidence: 99%

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A divide and conquer strategy for the maximum likelihood localization of low intensity objects

Krull¹,

Steinborn²,

Parton³

et al. 2014

Opt. Express

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show abstract

“…The energy of the PRNU in a CMOS will be affected by other sources of noises and the average power (variance) of the PRNU can be reduced [20]. The PRNU in CCD was measured by calculating the variance (σ 2 ) of the noise in 100 image sets [21]. The energy of the PRNU can be estimated to be in the range of 0.01% to 1.5%, depending on the type of image and sensor.…”

Section: Signature Extraction Modelmentioning

confidence: 99%

Mobile Camera Source Identification with SVD

Soobhany

Lam

Fletcher

et al. 2014

Lecture Notes in Electrical Engineering

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Abstract-A novel method for extracting the characterising sensor pattern noise (SPN) from digital images is presented. Based on the spectral decomposition technique of Singular Value Decomposition, the method estimates the SPN of each image in terms of its energy level by first transforming the image/signals into a linear additive noise model that separates the photo response non-uniformity (PRNU) of the associated camera from the signal subspace. The camera reference signatures of the individual cameras are computed from a sample of their respective images and compared with a mixture of image signatures from a set of known camera devices. The statistical properties of the method were studied using the Student's t-test constructed under the null hypothesis formalism. Our studies show that it is possible to determine the source device of digital images from camera phones using such method of signature extraction, with encouraging results.

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“…Eventually, after inverting the CRF, the image values are restored to the LS, where the dependencies of pixel values on incident light become linear again. More complex noise models accounting for a wider range of phenomena can be devised [19], with the drawback that model-based analytical solutions may become intractable [6].…”

Section: Imaging Model Of Ccd Camerasmentioning

confidence: 99%

Unsupervised estimation of signal-dependent CCD camera noise

Aiazzi

Alparone

Baronti

et al. 2012

EURASIP J. Adv. Signal Process.

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This article deals with an original method to estimate the noise introduced by optical imaging systems, such as CCD cameras. The power of the signal-dependent photon noise is decoupled from the power of the signal-independent electronic noise. The method relies on the multivariate regression of sample mean and variance. Statistically similar image pixels, not necessarily connected, produce scatterpoints that are clustered along a straight line, whose slope and intercept measure the signal-dependent and signal-independent components of the noise power, respectively. Experimental results carried out on a simulated noisy image and on true data from a commercial CCD camera highlight the accuracy of the proposed method and its applicability to separate R-G-B components that have been corrected for the nonlinear effects of the camera response function, but not yet interpolated to the the full size of the mosaiced R-G-B image.

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A model for measurement of noise in CCD digital-video cameras

Cited by 55 publications

References 17 publications

A divide and conquer strategy for the maximum likelihood localization of low intensity objects

A divide and conquer strategy for the maximum likelihood localization of low intensity objects

Mobile Camera Source Identification with SVD

Unsupervised estimation of signal-dependent CCD camera noise

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