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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
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) < ∞
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
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
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)
Sampling and Quantization
Sampling and Quantization
Digital Image Representation
N: No. of Columns
M: No. of Rows
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
Digital Image Representation
Number of bits required to store a digitized image
b = M x N x k
When M = N
b = N2k
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
Effects of Reducing Spatial Resolution
Effect of reducing
Intensity Resolution
Effect of reducing
Intensity Resolution
Level of Details
Required in image resizing such as shrinking and
zooming
Using known data to estimate data at unknown points
Interpolation
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
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