A Comprehensive Tool for Modeling CMOS Image-Sensor-Noise Performance

Gow, R.D.; Renshaw, D.; Findlater, K.; Grant, Lindsay A.; McLeod, Stuart J.; Hart, J.; Nicol, Robert L.

doi:10.1109/ted.2007.896718

Cited by 114 publications

(71 citation statements)

References 17 publications

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“…The combined assumption of η lens ¼ 0.95, γ ff ¼ 0.95 and η qe ¼ 0.5 leads to a minimum pixel size of d p;min ¼ 1.34 μm. With mass-market sensors and additional noise, 8 larger pixels are required. These small pixels also reach another technological limit of decreasing full well capacity.…”

Section: Results With Current Technologymentioning

confidence: 99%

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Photometric limits for digital camera systems

Schöberl

Brückner

Foessel

et al. 2012

J. Electron. Imaging

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Abstract. Image sensors for digital cameras are built with ever decreasing pixel sizes. The size of the pixels seems to be limited by technology only. However, there is also a hard theoretical limit for classical video camera systems: During a certain exposure time only a certain number of photons will reach the sensor. The resulting shot noise thus limits the signal-to-noise ratio. In this letter we show that current sensors are already surprisingly close to this limit. © 2012 SPIE and IS&T.

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Section: Results With Current Technologymentioning

confidence: 99%

“…In CCD or CMOS technology there are further sources of sensor noise, 8 which are neglected in the ideal case. SNR is a parameter that is directly visible in the final images.…”

Section: Image Acquisition Modelmentioning

confidence: 99%

Photometric limits for digital camera systems

Schöberl

Brückner

Foessel

et al. 2012

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…When shot noise is the dominant noise source, system noise can be viewed as white Gaussian noise. 27,28 Hence, our goal is to estimate the location of the camera receiver for white Gaussian noise, to obtain the MLE, and finally derive the CRLB.…”

Section: System Modelmentioning

confidence: 99%

Maximum likelihood estimation of vehicle position for outdoor image sensor-based visible light positioning system

Zhao

Lin

2016

Opt. Eng

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Abstract. Image sensor-based visible light positioning can be applied not only to indoor environments but also to outdoor environments. To determine the performance bounds of the positioning accuracy from the view of statistical optimization for an outdoor image sensor-based visible light positioning system, we analyze and derive the maximum likelihood estimation and corresponding Cramér-Rao lower bounds of vehicle position, under the condition that the observation values of the light-emitting diode (LED) imaging points are affected by white Gaussian noise. For typical parameters of an LED traffic light and in-vehicle camera image sensor, simulation results show that accurate estimates are available, with positioning error generally less than 0.1 m at a communication distance of 30 m between the LED array transmitter and the camera receiver. With the communication distance being constant, the positioning accuracy depends on the number of LEDs used, the focal length of the lens, the pixel size, and the frame rate of the camera receiver.

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“…Pixels with impulse noise should absorb the color, uniformity and edginess of their neighbors. Impulse noise must be taken into account since it may appear in imaging sensors [44].…”

Section: Perceptual Criteria For Propagating Local Information In Colmentioning

confidence: 99%

Edge-preserving color image denoising through tensor voting

Moreno

García

Puig

et al. 2011

Computer Vision and Image Understanding

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This paper presents a new method for edge-preserving color image denoising based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information through tensors in order to propagate them in a neighborhood by using a specific voting process. This voting process is specifically designed for edge-preserving color image denoising by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Measurements of removed noise, edge preservation and undesirable introduced artifacts, additionally to visual inspection, show that the proposed method has a better performance than the state-of-the-art image denoising algorithms for images contaminated with CCD camera noise.

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A Comprehensive Tool for Modeling CMOS Image-Sensor-Noise Performance

Cited by 114 publications

References 17 publications

Photometric limits for digital camera systems

Photometric limits for digital camera systems

Maximum likelihood estimation of vehicle position for outdoor image sensor-based visible light positioning system

Edge-preserving color image denoising through tensor voting

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