Noise Reduction Method for Image Signal Processor Based on Unified Image Sensor Noise Model

Yeul-Min BAEK; Whoi-Yul KIM

doi:10.1587/transinf.E96.D.1152

Noise Reduction Method for Image Signal Processor Based on Unified Image Sensor Noise Model

Yeul-Min BAEK, Whoi-Yul KIM

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The noise in digital images acquired by image sensors has complex characteristics due to the variety of noise sources. However, most noise reduction methods assume that an image has additive white Gaussian noise (AWGN) with a constant standard deviation, and thus such methods are not effective for use with image signal processors (ISPs). To efficiently reduce the noise in an ISP, we estimate a unified noise model for an image sensor that can handle shot noise, dark-current noise, and fixed-pattern noise (FPN) together, and then we adaptively reduce the image noise using an adaptive Smallest Univalue Segment Assimilating Nucleus ( SUSAN ) filter based on the unified noise model. Since our noise model is affected only by image sensor gain, the parameters for our noise model do not need to be re-configured depending on the contents of image. Therefore, the proposed noise model is suitable for use in an ISP. Our experimental results indicate that the proposed method reduces image sensor noise efficiently.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.5 pp.1152-1161

Publication Date: 2013/05/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.1152

Type of Manuscript: PAPER

Category: Image Processing and Video Processing

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Yeul-Min BAEK, Whoi-Yul KIM, "Noise Reduction Method for Image Signal Processor Based on Unified Image Sensor Noise Model" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 5, pp. 1152-1161, May 2013, doi: 10.1587/transinf.E96.D.1152.
Abstract: The noise in digital images acquired by image sensors has complex characteristics due to the variety of noise sources. However, most noise reduction methods assume that an image has additive white Gaussian noise (AWGN) with a constant standard deviation, and thus such methods are not effective for use with image signal processors (ISPs). To efficiently reduce the noise in an ISP, we estimate a unified noise model for an image sensor that can handle shot noise, dark-current noise, and fixed-pattern noise (FPN) together, and then we adaptively reduce the image noise using an adaptive Smallest Univalue Segment Assimilating Nucleus ( SUSAN ) filter based on the unified noise model. Since our noise model is affected only by image sensor gain, the parameters for our noise model do not need to be re-configured depending on the contents of image. Therefore, the proposed noise model is suitable for use in an ISP. Our experimental results indicate that the proposed method reduces image sensor noise efficiently.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1152/_p

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@ARTICLE{e96-d_5_1152,
author={Yeul-Min BAEK, Whoi-Yul KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Noise Reduction Method for Image Signal Processor Based on Unified Image Sensor Noise Model},
year={2013},
volume={E96-D},
number={5},
pages={1152-1161},
abstract={The noise in digital images acquired by image sensors has complex characteristics due to the variety of noise sources. However, most noise reduction methods assume that an image has additive white Gaussian noise (AWGN) with a constant standard deviation, and thus such methods are not effective for use with image signal processors (ISPs). To efficiently reduce the noise in an ISP, we estimate a unified noise model for an image sensor that can handle shot noise, dark-current noise, and fixed-pattern noise (FPN) together, and then we adaptively reduce the image noise using an adaptive Smallest Univalue Segment Assimilating Nucleus ( SUSAN ) filter based on the unified noise model. Since our noise model is affected only by image sensor gain, the parameters for our noise model do not need to be re-configured depending on the contents of image. Therefore, the proposed noise model is suitable for use in an ISP. Our experimental results indicate that the proposed method reduces image sensor noise efficiently.},
keywords={},
doi={10.1587/transinf.E96.D.1152},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Noise Reduction Method for Image Signal Processor Based on Unified Image Sensor Noise Model
T2 - IEICE TRANSACTIONS on Information
SP - 1152
EP - 1161
AU - Yeul-Min BAEK
AU - Whoi-Yul KIM
PY - 2013
DO - 10.1587/transinf.E96.D.1152
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2013
AB - The noise in digital images acquired by image sensors has complex characteristics due to the variety of noise sources. However, most noise reduction methods assume that an image has additive white Gaussian noise (AWGN) with a constant standard deviation, and thus such methods are not effective for use with image signal processors (ISPs). To efficiently reduce the noise in an ISP, we estimate a unified noise model for an image sensor that can handle shot noise, dark-current noise, and fixed-pattern noise (FPN) together, and then we adaptively reduce the image noise using an adaptive Smallest Univalue Segment Assimilating Nucleus ( SUSAN ) filter based on the unified noise model. Since our noise model is affected only by image sensor gain, the parameters for our noise model do not need to be re-configured depending on the contents of image. Therefore, the proposed noise model is suitable for use in an ISP. Our experimental results indicate that the proposed method reduces image sensor noise efficiently.
ER -