&lt;title&gt;Characterization of digital image noise properties based on RAW data&lt;/title&gt;

Hytti, Heli

doi:10.1117/12.640500

Cited by 32 publications

(18 citation statements)

References 4 publications

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“…An old and widely used camera performance analysis method is based on the photon transfer curve (PTC) [7]. Methods similar to the one used in this study have been applied in [5]. The PTC method generates a curve showing the standard deviation of an image sensor pixel value in different signal levels.…”

Section: Noise Metricmentioning

confidence: 99%

The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

Kurimo¹,

Lepistö

Nikkanen

et al. 2009

Lecture Notes in Computer Science

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Abstract. Motion blur and signal noise are probably the two most dominant sources of image quality degradation in digital imaging. In low light conditions, the image quality is always a tradeoff between motion blur and noise. Long exposure time is required in low illumination level in order to obtain adequate signal to noise ratio. On the other hand, risk of motion blur due to tremble of hands or subject motion increases as exposure time becomes longer. Loss of image brightness caused by shorter exposure time and consequent underexposure can be compensated with analogue or digital gains. However, at the same time also noise will be amplified. In relation to digital photography the interesting question is: What is the tradeoff between motion blur and noise that is preferred by human observers? In this paper we explore this problem. A motion blur metric is created and analyzed. Similarly, necessary measurement methods for image noise are presented. Based on a relatively large testing material, we show experimental results on the motion blur and noise behavior in different illumination conditions and their effect on the perceived image quality.

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Section: Noise Metricmentioning

confidence: 99%

The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

Kurimo¹,

Lepistö

Nikkanen

et al. 2009

Lecture Notes in Computer Science

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“…When the intensity is higher than d offset , the unified noise should consist of dark-current noise, shot noise, and FPN. However, since shot noise is associated with the portion of dark-current noise [18], only the shot noise and FPN terms are added when the intensity is higher than d offset . In most cases, L is bigger than d offset and σ D (t) has a small variance.…”

Section: Unified Noise Termsmentioning

confidence: 99%

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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“…However, manufacturing variability and component tolerances make these quantities vary from pixel to pixel, yielding the so-called dark signal nonuniformity (DSNU) and photo response nonuniformity (PRNU) [47,23,25]. These departures from ideality amount in (1.3) (and in the following calculations) to considering γ t , g, and δ as functions of (x, y), in order to take into account the nonuniformity of, respectively, quantum efficiency, gain, and dark signal.…”

Section: Spatial Nonuniformitymentioning

confidence: 99%

“…Estimating the whole set of noise parameters can be achieved by the so-called "photon transfer method" [47,25] which needs a controlled experimental setting and several data acquisitions. Estimating the slope g and the intercept σ 2 − gμ in (1.3) is sufficient for many applications.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering

Sur¹,

Grédiac²

2015

SIAM J. Imaging Sci.

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Abstract. This paper discusses camera noise estimation from a series of raw images of an arbitrary natural static scene, acquired with the same camera settings. Although it seems natural to characterize noise from the random time fluctuation of pixel intensity, it turns out that these fluctuations may also be caused by illumination flickering and mechanical microvibrations affecting the camera. In this context, the contributions are twofold. First, a theoretical model of image formation in the presence of illumination flickering and of vibrations is discussed. This parametric model is based on a Cox process. It is shown that illumination flickering changes the standard affine relation between noise variance and average intensity to a quadratic relation. Second, under these conditions an algorithm is proposed to estimate the main parameters governing sensor noise, namely the gain, the offset, and the readout noise. The rolling shutter effect, which potentially affects the output of any focal-plane shutter camera, is also considered. Experiments show that this simple method gives results consistent with the photon transfer method, which needs a special experimental setting and several data acquisitions, and with an algorithm based on a single image. The main practical result is to show that flickering, which is generally considered as an artifact, here plays a positive role since it finally enables us to estimate any of the sensor parameters.

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<title>Characterization of digital image noise properties based on RAW data</title>

Cited by 32 publications

References 4 publications

The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

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

Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering

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