Integrated Noise Modeling for Image Sensor Using Bayer Domain Images

Baek, Yeul-Min; Kim, Joong-Geun; Cho, Dong-Chan; Lee, Jin-Aeon; Kim, Whoi-Yul

doi:10.1007/978-3-642-01811-4_37

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…We used the simple version of the noise model as in Eq. (22). Thus, only two images were used for the dark-current noise model.…”

Section: Experimental Results On Noise Modelmentioning

confidence: 99%

“…In our previous approach [22], we modeled dark-current noise, shot noise, and FPN together. However, the approach needs too many images for dark-current noise modeling and has high computational complexity due to iterative square root and exponential operations for reducing image noise.…”

Section: Unified Noise Modelingmentioning

confidence: 99%

“…(21) or Eq. (22). Our noise model is a function of the noiseless intensity L. However, the observation intensity is corrupted by noise as expressed in Eq.…”

Section: Adaptive Susan Filtermentioning

confidence: 99%

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

Baek

Kim

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThe 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. key words: image signal processor, noise modeling, denoising, image sensor, SUSAN filtering

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“…We used the simple version of the noise model as in Eq. (22). Thus, only two images were used for the dark-current noise model.…”

Section: Experimental Results On Noise Modelmentioning

confidence: 99%

Section: Unified Noise Modelingmentioning

confidence: 99%

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

Baek

Kim

2013

IEICE Trans. Inf. & Syst.

Self Cite

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X-ray fluoroscopy noise modeling for filter design

et al. 2012

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Fluoroscopic noise modeling is useful to design effective procedures for noise estimation and image filtering. In particular, filter performance analysis has showed that the knowledge of the noise model and the accurate estimate of noise characteristics can significantly improve the image restoration, especially for edge preserving. Fluoroscopic image enhancement can support further X-ray exposure reduction, medical image analysis and automated object identification (i.e., surgery tools, anatomical structures).

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