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Transcript of Lecture 3 Digital Image Fundamentals Dr. Arslan Shaukat · PDF fileDigital Image Fundamentals...
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EC-433 Digital Image Processing
Lecture 3
Digital Image Fundamentals
Dr. Arslan Shaukat
Acknowledgement: Lecture slides material from
Dr. Rehan Hafiz, Dr. Imtiaz Taj, Wanasanan Thongsongkrit
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A Simple Image Formation Model
Image refers to a 2D light-intensity function, f(x,y).
The amplitude of f at spatial coordinates (x,y) gives the
intensity of the image at that point.
f(x,y) must be nonzero and finite, i.e. 0 < f(x,y) < ∞
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Image Formation Model
f(x,y) may be characterized by 2 components:
– Illumination, i(x,y): the amount of source light incident on the
scene being viewed
– Reflectance, r(x,y): the amount of light reflected by the objects
in the scene
f (x, y) = i(x, y) r(x, y)
0 < i(x, y) < ∞: determined by the nature of the light
source
0 < r(x, y) < 1: determined by the nature of the object
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Image Formation Model
We call the intensity of a monochrome image f at
coordinate (x,y), the gray level (l) of the image at that
point.
Thus, l lies in the range Lmin ≤ l ≤ Lmax
Lmin is positive and Lmax is finite.
Gray scale = [Lmin, Lmax]
Common practice, shift the interval to [0, L]
0 = black , L = white
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Image Sampling and Quantization
An image may be continuous with respect to the x- and y-
coordinates, and also in amplitude.
To convert it to digital form, we have to sample the
function in both coordinates and in amplitude.
Sampling: Digitizing the coordinate values.
Quantization: Digitizing the amplitude values.
– 8 bit quantization: 28 =256 gray levels (0: black, 255: white)
– Binary (1 bit quantization):2 gray levels (0: black, 1: white)
Commonly used number of samples (resolution)
– Digital still cameras: 640x480, 1024x1024, up to 4064 x 2704
– Digital video cameras: 640x480 at 30 frames/second
1920x1080 at 60 f/s (HDTV)
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Sampling and Quantization
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Sampling and Quantization
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Digital Image Representation
N: No. of Columns
M: No. of Rows
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Digital Image Representation
L intensity or gray-levels
– L = 2k
– K-bit image
– Integer values [0, L-1]
– Dynamic Range
• Range of values spanned by the
gray scale
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Digital Image Representation
Number of bits required to store a digitized image
b = M x N x k
When M = N
b = N2k
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Spatial Resolution
– Smallest discernible detail in an image
– Defined by spatial sampling interval
– Dots (pixels) per unit distance or dots per inch (DPI) is a
measure of image resolution
Intensity Resolution
– Defined by the intensity quantization
– Number of gray levels is usually an integer power of 2
– Image whose intensity is quantized into 256 levels has 8 bits of
intensity resolution
Resolution
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Effects of Reducing Spatial Resolution
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Effect of reducing
Intensity Resolution
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Effect of reducing
Intensity Resolution
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Level of Details
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Required in image resizing such as shrinking and
zooming
Using known data to estimate data at unknown points
Interpolation
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Simply replicate the value from neighboring pixels
Nearest Neighbor Interpolation
1 0 1
1 1 0
1 0 1
1 0 1
1 1 0
1 0 1
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Severe distortion of straight edges
Nearest Neighbor Interpolation
1 0 1
1 1 0
1 0 1
1 1 0 0 0 1 1
1 1 0 0 0 1 1
1 1 1 1 1 0 0
1 1 1 1 1 0 0
1 1 1 1 1 0 0
1 1 0 0 0 1 1
1 1 0 0 0 1 1