CMOS imager technology shrinks and image performance

Rhodes, H.; Agranov, Gennadiy; Hong, Chris; Boettiger, U.; Mauritzson, Rick; Ladd, John; Karasev, Igor; McKee, J.; Jenkins, Edward B.; Quinlin, W.; Patrick, I.; Li, J.; Fan, Xiaofeng; Panicacci, Roger; Smith, S.; Mouli, Chandra; Bruce, J.

doi:10.1109/wmed.2004.1297338

Cited by 49 publications

(26 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is mainly achieved by an increase of the pixel density. Despite the electrical and thermal characteristics of the sensors have noticeably improved in the last decade [18], [15], with the size of each pixel becoming smaller and smaller the sensor output signal's susceptibility to photon noise has become greater and greater. As of now, this source of noise appears as the most signiÞcant contributor of the overall noise in a digital imaging sensor [1].…”

mentioning

confidence: 99%

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Foi

Trimeche

Katkovnik

et al. 2008

IEEE Trans. on Image Process.

755

667

View full text Add to dashboard Cite

Abstract-We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbancies in the output data. We further explicitly take into account the clipping of the data (over-and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images as well as with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.

show abstract

mentioning

confidence: 99%

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Foi

Trimeche

Katkovnik

et al. 2008

IEEE Trans. on Image Process.

755

667

View full text Add to dashboard Cite

show abstract

“…Optical diffraction refers to the phenomenon that at a high incidence angle of the light some photons stray away from the center of the pixel in question and get recorded by neighboring sensor(s) [7], [8] As such optical diffraction is location dependent. Minority carrier refers to the fact that electrons can escape from the target after the charge is collected [6].…”

Section: Problem Statementmentioning

confidence: 99%

“…In the camera industry the removal of color crosstalk, called decrosstalk in this paper, is performed by adopting more sophisticated manufacturing techniques [3], [6], [7], [8], and/or by empirically chosen linear transforms. But these solutions are costly and cannot eliminate the crosstalk problem completely.…”

Section: Introductionmentioning

confidence: 99%

Joint color decrosstalk and demosaicking for CFA cameras

Zhang

2010

2010 IEEE International Conference on Image Processing

View full text Add to dashboard Cite

In interest of low cost, low power consumption, and compact size, most digital cameras adopt a design of single sensor array coupled with a color filter array. This design inevitably suffers, due to physical characteristics of the optical and semiconductor components and the imperfection of manufacturing, from the problem of crosstalk between different color channels. Channel crosstalk can desaturate colors and blur image details, but the problem was seemingly overlooked by existing color demosaicking algorithms.To rectify this deficiency we propose a new joint demosaicking and decrosstalk technique that counters the effects of channel crosstalks by adaptive least-squares inverse filtering. The new technique integrates the operations of deconvolution for crosstalk removal and interpolation for color demosaicking, and it introduces a general framework in which any spatially varying crosstalks and varying spatial-spectral correlations can be modeled and factored into the color reproduction. Simulation results show that the proposed technique is highly effective and capable to obtain both high color fidelity and sharp, clean spatial details.

show abstract

“…15 For a monochrome sensor, there must be two samples per Airy disk diameter. Thus, the pixel pitch must equal the radius of the Airy disk.…”

Section: Diffraction Sampling and Colormentioning

confidence: 99%

Roadmap for CMOS image sensors: Moore meets Planck and Sommerfeld

Catrysse

Wandell

2005

SPIE Proceedings

View full text Add to dashboard Cite

The steady increase in CMOS imager pixel count is built on the technology advances summarized as Moore's law. Because imagers must interact with light, Moore's Law impact differs from its impact on other integrated circuit applications. In this paper, we investigate how the trend towards smaller pixels interacts with two fundamental properties of light: photon noise and diffraction. Using simulations, we investigate three consequences of decreasing pixel size on image quality. First, we quantify the likelihood that photon noise will become visible and derive a noisevisibility contour map based on photometric exposure and pixel size. Second, we illustrate the consequence of diffraction and optical imperfections on image quality and analyze the implications of decreasing pixel size for aliasing in monochrome and color sensors. Third, we calculate how decreasing pixel size impacts the effective use of microlens arrays and derive curves for the concentration and redirection of light within the pixel.

show abstract

CMOS imager technology shrinks and image performance

Cited by 49 publications

References 2 publications

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Joint color decrosstalk and demosaicking for CFA cameras

Roadmap for CMOS image sensors: Moore meets Planck and Sommerfeld

Contact Info

Product

Resources

About