Analysis of noise in CMOS image sensor

Brouk, Igor; Nemirovsky, Amikam; Nemirovsky, Y.

doi:10.1109/comcas.2008.4562800

Cited by 18 publications

(14 citation statements)

References 6 publications

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“…Regarding the relative importance of each noise source, various articles agree in concluding that under low illumination conditions, the primary noise source is the reset noise, while for high illumination the major noise source is the photon shot noise [2,9]. For the values normally taken by λ i = aτ i C + dD i , the Poisson distribution can be correctly approximated by a Gaussian distribution with mean and variance equal to λ i .…”

mentioning

confidence: 94%

“…We show, however, that there is not much room for improvement. 2. Surprisingly, all the methods proposed in the literature discard saturated samples.…”

mentioning

confidence: 99%

“…For the values normally taken by λ i = aτ i C + dD i , the Poisson distribution can be correctly approximated by a Gaussian distribution with mean and variance equal to λ i . Moreover, the dark current can be neglected for exposure times below 1s [14] and the quantization noise is negligible compared to the readout noise [2,9]. Notice that the structured nature of the quantization noise (it is not white) may make it noticeable after non linear post-processing.…”

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confidence: 99%

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Best Algorithms for HDR Image Generation. A Study of Performance Bounds

Aguerrebere¹,

Delon

Gousseau³

et al. 2014

SIAM J. Imaging Sci.

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Abstract. Since the seminal work of Mann and Picard in 1994, the standard way to build high dynamic range (hdr) images from regular cameras has been to combine a reduced number of photographs captured with different exposure times. The algorithms proposed in the literature differ in the strategy used to combine these frames. Several experimental studies comparing their performances have been reported, showing in particular that a maximum likelihood estimation yields the best results in terms of mean squared error. However, no theoretical study aiming at establishing the performance limits of the hdr estimation problem has been conducted. Another common aspect of all hdr estimation approaches is that they discard saturated values. In this paper, we address these two issues. More precisely, we derive theoretical bounds for the hdr estimation problem, and we show that, even with a small number of photographs, the maximum likelihood estimator performs extremely close to these bounds. As a second contribution, we propose a general strategy to integrate the information provided by saturated pixels in the estimation process, hence improving the estimation results. Finally, we analyze the sensitivity of the hdr estimation process to camera parameters, and we show that small errors in the camera calibration process may severely degrade the estimation results.Key words. high dynamic range imaging, irradiance estimation, exposure bracketing, multi-exposure fusion, camera acquisition model, noise modeling, censored data, exposure saturation, Cramér-Rao lower bound.1. Introduction. The human eye has the ability to capture scenes of very high dynamic range, retaining details in both dark and bright regions. This is not the case for current standard digital cameras. Indeed, the limited capacity of the sensor cells makes it impossible to record the irradiance from very bright regions for long exposures. Pixels saturate incurring in information loss under the form of censored data. On the other hand, if the exposure time is reduced in order to avoid saturation, very few photons will be captured in the dark regions and the result will be masked by the acquisition noise. Therefore the result of a single shot picture of a high dynamic range scene, taken with a regular digital camera, contains pixels which are either overexposed or too noisy.High dynamic range imaging (hdr for short) is the field of imaging that seeks to accurately capture and represent scenes with the largest possible irradiance range. The representation problem of how to display an hdr image or irradiance map in a lower range image (for computer monitors or photographic prints) while retaining localized contrast, known as tone mapping, will not be addressed here. Due to technological and physical limitations of current optical sensors, nowadays the most common way to reach high irradiance dynamic ranges is by combining multiple low dynamic range photographs, acquired with different exposure times τ 1 , τ 2 , . . . , τ T . Indeed, for a given irradiance C and expos...

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confidence: 94%

“…We show, however, that there is not much room for improvement. 2. Surprisingly, all the methods proposed in the literature discard saturated samples.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Best Algorithms for HDR Image Generation. A Study of Performance Bounds

Aguerrebere¹,

Delon

Gousseau³

et al. 2014

SIAM J. Imaging Sci.

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“…Both types use the junction capacitor of a photodiode to integrate its current and measure the change in its voltage after a fixed time interval which depends linearly on the incident light intensity. The 3T APS uses 3 MOS transistors and can be fabricated in a standard low cost CMOS process but suffers from dark current and reset noises [9,11,12]. The more popular 4T APS uses 4 MOS transistors and replaces the photodiode with a "pinned" photodiode [13] which inherently has lower dark current but requires special fabrication steps.…”

Section: Active Pixel Sensor (Aps)mentioning

confidence: 99%

“…Note that dark current disappears when the photodiode is operated at zero voltage. This paper focuses on a pixel structure that produces low dark current since it not only degrades optical dynamic range, but also contributes noise [9,11,12]. In particular, 2 types of pixel structure are reviewed.…”

Section: Introductionmentioning

confidence: 99%

A Zero Bias Pixel Sensor and its Zero-Bias Column Buffer-Direct-Injection Circuit

Suriyavejwongs¹,

Leelarasmee²,

Pora³

2017

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Abstract. Two pixel sensors, namely active pixel sensor (APS) and pseudo-active pixel sensor (PAPS), are reviewed to show that APS suffers from dark current while PAPS suffers from leakage current. Then a new pixel sensor called zero bias pixel sensor (ZBPS) in which only two MOS switches in addition to the photodiode are used, one for connecting the pixel's photodiode to a column bus and the other for bypassing it. A zero-bias column buffer-direct-injection (ZCBDI) circuit, which is similar to a regulated cascode amplifier, is used to control the voltage at column bus at zero. All ZBPS pixels are guaranteed to work at zero voltage at all times to eliminate the dark current as well as leakage current. A case of a 10µm x 10µm ZBPS pixel designed with standard 0.18µm CMOS process is studied through simulation. This pixel generates a photocurrent within a range from 1pA to 100nA. To handle a large variation of photocurrent while maintaining zero column voltage, the ZCBDI is designed using differential cascode, common source, and buffer stages and then compensated for 50 degree phase margin. Transient simulation shows that the pixel steady state response time is around 1.406ms, leading to at most 5.5 frames per second for an image of 128x128 ZBPS pixels. The fill factor of ZBPS for this case is around 59%.

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