Suppression of noise amplification during colour correction

Kharitonenko, I.; Twelves, S.; Weerasinghe, C.

doi:10.1109/tce.2002.1010126

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…A challenge for many authors is to find a way to reduce this amplification. This can be done by optimizing spectral sensitivities of the sensor [16][17][18] or by changing the way CCM is computed (adding regularization, tuning the CCM coefficients...) [9,19]. Complementary to these studies, the goal of our paper is mainly to describe the algebraic mechanisms that lead to this noise amplification.…”

Section: Example Of Noise Amplification Through Color Correctionmentioning

confidence: 99%

“…Once again, images are computed with high resolution multispectral images from the ReDFISh dataset [13]. For each sensor, color images have been computed with a normalized update of the CCM kernel whose calculus is based on Equation (19) such that no training set is used. In a second test, we perform the same benchmark but in dim light conditions.…”

Section: Parameters Valuesmentioning

confidence: 99%

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The Geometry of Noise in Color and Spectral Image Sensors

Clouet

Vaillant

Alleysson

2020

Sensors

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Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).

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Section: Example Of Noise Amplification Through Color Correctionmentioning

confidence: 99%

Section: Parameters Valuesmentioning

confidence: 99%

The Geometry of Noise in Color and Spectral Image Sensors

Clouet

Vaillant

Alleysson

2020

Sensors

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“…Color correction is performed using an algorithm developed by the authors, which is described in detail in [6]. The Green channel is unaltered.…”

Section: Color Correctionmentioning

confidence: 99%

“…Therefore there are 320 (20 x 16) blocks B(m,n) calculated according to (6). (6) where f(x, y) is the initial high-resolution frame acquired from image sensor and k is the block size, which is equal to 64. In other words, the reduced size frame is obtained by block based averaging of the captured high-resolution frame.…”

Section: Frame Size Reductionmentioning

confidence: 99%

A Prototype of Autonomous Intelligent Surveillance Cameras

Kharitonenko

Lichman

et al. 2006

2006 IEEE International Conference on Video and Signal Based Surveillance

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This paper presents an architecture and an FPGAbased prototype of an autonomous intelligent video surveillance camera. The camera takes the advantage of high resolution of CMOS image sensors and enables instantly automatic pan, tilt and zoom adjustment based upon motion activity. It performs automated scene analysis and provides immediate response to suspicious events by optimizing camera capturing parameters. The video output of the camera can be optimized to any region of interest while the camera continues to monitor the entire scene. Field trials of the prototyped camera have verified the proposed architecture. Disciplines Physical Sciences and Mathematics

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“…The proposed color correction algorithm reduces amplification of the noise. As CMOS image sensors are inherently noisy, this algorithm is especially beneficial for use with such sensors [8]. The color correction is often carried out using the following matrix: (8) As shown in Equation (8), subtracting weighted values of the other two channels performs the color correction for a particular channel.…”

Section: B Color Correction Modulementioning

confidence: 99%

Novel color processing architecture for digital cameras with CMOS image sensors

Weerasinghe¹,

Li²,

Kharitonenko³

et al. 2005

IEEE Trans. Consumer Electron.

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This paper presents a color processing architecture for digital color cameras utilizing complementary metal oxide semiconductor (CMOS) image sensors. The proposed architecture gives due consideration to the peculiar aspects of CMOS image sensors and the human visual perception related to the particular application of digital color photography. A main difference between the proposed method arid the conventional systems is the fact that color correction module is located before the interpolation module. Therefore, a method of performing color correction on a color filter array (CFA) pattern is also provided in this paper. The interpolation algorithm is especially designed to solve the problem of pixel cross talk among the pixels of different color channels. The algorithm separates the green channel into two planes, one highly correlated with the red channel and the other with the blue channel. These separate planes are used for red and blue channel interpolation. The implementation details related to managing four color channel values is also described. Experiments conducted on McBeth color chart and natural images have shown that the proposed color processing chain produces better quality images with improved SNR. Disciplines Physical Sciences and Mathematics Publication DetailsLi, W., Kharitonenko, I., Weerasinghe, C. Novel Color Processing Architecture for Digital Cameras with CMOS Image SensorsChaminda Weerasinghe, Wanqing Li, Igor Kharitonenko, Magnus Nilsson and Sue TwelvesAbstract -This paper presents a color processing architecture for digital color cameras utilizing Complementary Metal Oxide Semiconductor (CMOS) image sensors. The proposed architecture gives due consideration to the peculiar aspects of CMOS image sensors and the human visual perception related to the particular application of digital color photography. A main difference between the proposed method and the conventional systems is the fact that color correction module is located before the interpolation module. Therefore, a method of performing color correction on a color filter array (CFA) pattern is also provided in this paper. The interpolation algorithm is especially designed to solve the problem of pixel cross talk among the pixels of different color channels. The algorithm separates the green channel into two planes, one highly correlated with the red channel and the other with the blue channel. These separate planes are used for red and blue channel interpolation. The implementation details related to managing four color channel values is also described. Experiments conducted on McBeth color chart and natural images have shown that the proposed color processing chain produces better quality images with improved SNR.

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Suppression of noise amplification during colour correction

Cited by 8 publications

References 5 publications

The Geometry of Noise in Color and Spectral Image Sensors

The Geometry of Noise in Color and Spectral Image Sensors

A Prototype of Autonomous Intelligent Surveillance Cameras

Novel color processing architecture for digital cameras with CMOS image sensors

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