Image Processing Reference
In-Depth Information
1. A basis of camera-by-image identification is the analysis of features leaved to area of
pixels of the given image.
2. As an input image format for creation of image identifying the camera, and subsequent
identification of belonging the arbitrary checked image to the camera, the most suitable
is the raster format without any compression. In view of that similar formats of
representation are last formats at logical level for the majority of visual information
output devices. It is possible to convert any format of digital images without quality
loss.
Thus, for digital photocameras it is possible to select two classes of features which could be
used as a basis for identification:
1. Hardware features are reflections of deviations of characteristics of a sensor control
steady in time and the subsequent units of handling, including ADC, as separate device
in the received digital image. Generally sensor control signs allow to identify a specific
copy of the device. In particular for digital cameras those are defects and deviations
within tolerances of separate photosensitive elements, defects of elements of the unit of
a photosensitive matrix [16, 20].
2. Features of postprocessing algorithms. The digital image received at output of ADC of
digital cameras is then further processed. In digital cameras algorithms of the
postprocessing that make the greatest impact on the resulted image are algorithms of
image recovery from a mosaic (Bayer) structure of a sensor [17], algorithms of
increasing contour sharpness and noise reduction. In the majority of the most
widespread photocameras of the lower price segment algorithms of postprocessing can
not be switched off and the only image formats accessible outside the camera are JPEG
or processed TIFF.
In view of that algorithms of postprocessing are the general sometimes for all models of one
vendor [16, 23], for detection by sample-unique features it is necessary to take identification
on parameters of an analog section, i.e. on the first class of features.
1.4 Methods of matrix data-to-image conversion
Let's consider used algorithmic primitives of interpolation the colors applied to form the
color image in digital photographic cameras.
Let light filters of primary colors are allocated in Bayer's grid according to a picture 1.
The algorithms used for recovery of missing color components, are represent "know-how" of
vendors and, as a rule, vary depending on model of the camera and type of a photosensitive
matrix. However most often they are constructed on the basis of linear and median
filtrations primitives, threshold gradients and persistence of color tone.
r(1,1)
g(1,2)
r(1,3)
g(1,4)
r(1,5)
g(1,6)
g(2,1)
b(2,2)
g(2,3)
b(2,4)
g(2,5)
b(2,6)
r(3,1)
g(3,2)
r(3,3)
g(3,4)
r(3,5)
g(3,6)
g(4,1)
b(4,2)
g(4,3)
b(4,4)
g(4,5)
b(4,6)
r(5,1)
g(5,2)
r(5,3)
g(5,4)
r(5,5)
g(5,6)
g(6,1)
b(6,2)
g(6,3)
b(6,4)
g(6,5)
b(6,6)
Fig. 1. Color filter array in the Bayer structure
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