Noise Measurement for Raw-Data of Digital Imaging Sensors by Automatic Segmentation of Nonuniform Targets

Abstract

In this paper, we present a new method for measuring the temporal noise in the raw-data of digital imaging sensors [e.g., CMOS and charge-coupled device (CCD)]. The method is specially designed to estimate the variance function which describes the signal-dependent noise found in raw-data. It gives the standard-deviation of the noise as a function of the expectation of the pixel raw-data output value.In contrast with established methods (such as the ISO 15739), our method does not require the use of a specific target or a particular calibration. This is possible due to an automatic segmentation embedded in the data analysis.We show experimental results for the raw-data from two different CMOS sensors of commercial cameraphones.

Uniform Motion Blur in Poissonian Noise: Blur/Noise Tradeoff

“…This paper aims at filling this gap, by introducing an image formation model that describes the interplay between noise, blur, and signal intensity as the exposure time varies. This model is particularly suited for the raw data from digital imaging sensors (see, e.g., [9], [10]). The proposed model allows to evaluate the trade-off between noise and blur, aiming at establishing an optimal exposure time for which the image quality can be maximized by means of a deconvolution algorithm.…”

Spatially adaptive color filter array interpolation for noiseless and noisy data

“…This noise can be written explicitly in the additive form (1) where the standard deviation depends on the image intensity as r bayer ði; jÞ ¼ stdfz bayer ði; jÞg ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Wfy RGB ði; jÞg=w p . It is shown in (Foi et al, 2006(Foi et al, , 2007) that such a model can be used for generic CMOS digital imaging sensors. c. The nonstationary Gaussian noise with the signal-dependant standard deviation r bayer (i,j) Parks, 2005b, 2006).…”

“…The noise model and its parameters were identified exactly in the same way how it is done in (Foi et al, 2006(Foi et al, , 2007. The left image was interpolated by HA CFAI (Hamilton and Adams, 1997) and the right by the proposed CFAI for noisy data.…”

“…This digital output of the sensor is called ''raw data. '' It is known that the raw data from the sensor is corrupted by signal-dependant noise (Hirakawa and Parks, 2005b;Foi et al, 2006Foi et al, , 2007. Such modeling is common for charge-coupled device (CCD) and complementary-symmetry/metal-oxide semiconductor (CMOS) digital image sensors.…”

“…Such modeling is common for charge-coupled device (CCD) and complementary-symmetry/metal-oxide semiconductor (CMOS) digital image sensors. A property of such a noise is the dependence of its variance from the signal (Foi, 2007). The most widely encountered models for this dependence are film-grain, multiplicative, speckle noise, and, in particular, Poissonian noise.…”