Download Digital Image Processing: Digital Imaging Fundamentals

Chapter 2
2012
Teacher: Remah W. Al-Khatib
This lecture will cover:
 The human visual system
 Light and the electromagnetic spectrum
 Image representation
 Image sensing and acquisition
 Sampling, quantisation and resolution
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The best vision model we have!
It is one of the most sophisticated image
processing and analysis systems.
Knowledge of how images form in the eye
can help us with processing digital images
Its understanding would also help in the
design of efficient, accurate and effective
computer/machine vision systems.
In the following slides we will consider what is
involved in capturing a digital image of a real
world scene:
 Image sensing and representation
 Sampling and quantisation
 Resolution
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A typical image formation system consists of an
illumination” source, and a sensor.
Energy from the illumination source is either
reflected or absorbed by the object or scene, which is
then detected by the sensor.
Depending on the type of radiation used, a photo
converter (e.g., a phosphor screen) is typically used
to convert the energy into visible light.
Sensors that provide digital image as output, the
incoming energy is transformed into a voltage
waveform by a sensor material that is responsive to
the particular energy radiation.
The voltage waveform is then digitized to obtain
adiscrete output.
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Incoming energy is transformed into a
voltage by the combination of input electrical
power and sensor material.
Continuous image to be converted into digital
form:
Sampling: digitize the coordinate values
Quantization: digitize the amplitude values
Issues in sampling and quantization, related to
sensors.
Conventions
 Origin at the top
 left corner
 x increases from
left to right
 y increases from
top to bottom
 Each element of
the matrix array is
called a pixel, for
picture element
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Matrix form
bits to store the image = M x N x k
gray level = 2k
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L-level digital image of size MxN
= digital image having
• a spatial resolution MxN pixels
• a gray-level resolution of L levels
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Spatial resolution determined by sampling
• Smallest discernible detail in an image
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Gray-level resolution determined by number
of gray scales
• Smallest change in gray level
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Down-sampling
•Up-sampling
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2k-level digital image of size NxN
How K and N affect the image quality
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How many samples and gray levels are
required for a good approximation?
Quality of an image depends on number of
pixels and gray-level number
i.e. the more these parameters are increased,
the closer the digitized array approximates
the original image.
But: Storage & processing requirements
increase rapidly as a function of N, M, and k
Operations applied to digital images:
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Zoom: up-sampling
• Pixel duplication
• Bi-linear interpolation
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Shrink: down-sampling