Image Processing Reference
In-Depth Information
Camera identification based on dark frame correction along with obvious advantage of
identification of concrete camera sample inherent critical disadvantage namely requirement
of dark frames to identify cameras, which makes this method nearly completely useless in
practical sense.
In [20] the camera identification method based on non-uniformity of pixel matrix namely
different photosensitivity of pixels.
There are many sources of defects and noises which are generated at different image
processing stages. Even if sensors form several images of absolutely static scene, the resulted
digital representations may posess insignificant alterations of intensity between ”same
pixel” of image. It appears partly from shot noise [14,15] which is random, and partially
because of structure non-uniformity, which is deterministic and slowly changed across even
very large sets of image for similar conditions.
Structural non-uniformity presented in every image and can be used for camera
identification. Due to similarity of non-uniformity's nature and random noise it is frequently
named structural noise.
By averaging multiple images context impact is reduced and structural noises are separated
structural matrix noise can be viewed as two components — fixed pattern noise (FPN) and
photo-response non-uniform noise (PRNU). Fixed pattern noise is induced by dark currents
and defined primarily by pixels non-uniformity in absence of light on sensitive sensor area.
Due to additive nature of FPN, modern digital cameras suppress it automatically by
subtracting the dark frame from every image [14]. FPN depends on matrix temperature and
time of exposure. Natural images primary structural noise component is PRNU. It is caused
by pixels non-uniformity (PNU), primarily non-uniform photosensitivity due to non-
homogeneity of silicon wafers and random fluctuations in sensor manufacturing process.
Source and character of noise induced by pixels non-uniformities make correlation of noise
extracted from two even identical matrixes small. Also temperature and humidity don't
render influence to PNU-noise. Light refraction on dust particles and optical system also
also induced its contribution to PRNU-noise, but these effects are not stable (dust can
migrate over the matrix surface, vignette type changes with focal length or lens change)
hence, can't be used for reliable identification.
The model of image obtaining process is the following. Let absolute photon number on
pixel's area with coordinates (i, j) corresponds
x
, where i ..m, j ..n
, m  —
ij
photosensitive matrix resolution. If we designate shooting noise as
i  , additive noise due to
reading and other noises as
i  , dark currents as ij
c
. Then sensor's output
y
is:
ij
yf ( x
   .
)
c
(*)
ij
ij
ij
ij
ij
ij
Here i f is almost 1 and is multiplicative PRNU-noise.
Final image pixels
i p are completely formed after multiple-stage processing of
y
ij
including, interpolation over adjacent pixels, color correction and image filtering. Many of
that operations are non-linear like gamma correction white balance estimation, adaptive
Bayer structure interpolation based on strategies for missing color recoveries. So:
p
P( y ,N(y ),i , j )
,
ij
ij
ij
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