Very Low Dark Current CCD Image Sensor

Bogaart, E.W.; Hoekstra, W.; Peters, Inge M.; Kleimann, A.C.; Bosiers, Jan T.

doi:10.1109/ted.2009.2030642

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…Under normal operation, however, the number of photon-induced electrons is combined with dark current electrons caused by the physical properties of the PN junction. Three primary sources generate this dark current: irregularities in the silicon structure, diffusion current due to Fick's law and depletion region current which follows Ohm's Law [20].…”

Section: Semi-conductor Physicsmentioning

confidence: 99%

Rethinking Image Sensor Noise for Forensic Advantage

Matthews,

Sorell,

Falkner

2018

Preprint

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Sensor pattern noise has been found to be a reliable tool for providing information relating to the provenance of an image. Conventionally sensor pattern noise is modelled as a mutual interaction of pixel non-uniformity noise and dark current. By using a wavelet denoising filter it is possible to isolate a unique signal within a sensor caused by the way the silicon reacts non-uniformly to light. This signal is often referred to as a fingerprint. To obtain the estimate of this photo response non-uniformity multiple sample images are averaged and filtered to derive a noise residue. This process and model, while useful at providing insight into an images provenance, fails to take into account additional sources of noise that are obtained during this process. These other sources of noise include digital processing artefacts collectively known as camera noise, image compression artefacts, lens artefacts, and image content. By analysing the diversity of sources of noise remaining within the noise residue, we show that further insight is possible within a unified sensor pattern noise concept which opens the field to approaches for obtaining fingerprints utilising fewer resources with comparable performance to existing methods.

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Section: Semi-conductor Physicsmentioning

confidence: 99%

Rethinking Image Sensor Noise for Forensic Advantage

Matthews,

Sorell,

Falkner

2018

Preprint

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“…Nowadays the CCD sensors are optimized to achieve the lowest possible dark current by means of fabrication purity and by utilization of various pixel structures, which may be virtually free from defects (Bogaart et al (2009)). This results in a lower number of hot pixels and reduced average dark current.…”

Section: Stationary Dark Currentmentioning

confidence: 99%

On the efficiency of techniques for the reduction of impulsive noise in astronomical images

Kurek

Błachowicz

Orlov

et al. 2016

Mon. Not. R. Astron. Soc.

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The impulsive noise in astronomical images originates from various sources. It develops as a result of thermal generation in pixels, collision of cosmic rays with image sensor or may be induced by high readout voltage in Electron Multiplying CCD (EMCCD). It is usually efficiently removed by employing the dark frames or by averaging several exposures. Unfortunately, there are some circumstances, when either the observed objects or positions of impulsive pixels evolve and therefore each obtained image has to be filtered independently. In this article we present an overview of impulsive noise filtering methods and compare their efficiency for the purpose of astronomical image enhancement. The employed set of noise templates consists of dark frames obtained from CCD and EMCCD cameras working on ground and in space. The experiments conducted on synthetic and real images, allowed for drawing numerous conclusions about the usefulness of several filtering methods for various: (1) widths of stellar profiles, (2) signal to noise ratios, (3) noise distributions and (4) applied imaging techniques. The results of presented evaluation are especially valuable for selection of the most efficient filtering schema in astronomical image processing pipelines.

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“…According to the literature, the increase in dark current with T det approximately doubles for each 5 C increase in temperature. 26 This means that an exponential dependence is experimentally observed; 12,21,27 however, it corresponds to the systematic component, whereas k 1 is related to the random component of that dependence. To estimate the dependence of k 1 on T det , for instance, it can also be taken into account that the thermal dark current from a photomultiplier tube can be given by: 28…”

Section: Signal-to-noise and Temperaturementioning

confidence: 99%

“…Thus, little information is available regarding the design of experiments to identify how different factors affect uncertainty in uorescence measurements. Some studies related to accuracy and precision have been carried out for digital cameras; however, they mainly deal with the problem of luminescent background digital signals 12 through the use of the hue (H) parameter of HSV colour-space 13 or upconversion for ratiometric methods. 14 The dependence of uncertainty (noise, N) on the analytical signal (I S ) can be expressed as: 1,7…”

Section: Introductionmentioning

confidence: 99%