Digital Image Processing:Digital Imaging Fundamentals
Chapter 22012
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
Contents
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.
Human Visual System
Illustration of Human Eye
Illustration of Human Eye
Image Formation in eye andcamera
Image formation in the Eye
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
Sampling, Quantisation AndResolution
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.
Image Sensing and Acquisition
Images
Incoming energy is transformed into a voltage by the combination of input electrical power and sensor material.
Image Sensors
Basic Concepts in Image Samplingand Quantization
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
Representing digital images
Matrix form
Representing digital images(cont.)
bits to store the image = M x N x kgray level = 2k
L-level digital image of size MxN = digital image having• a spatial resolution MxN pixels• a gray-level resolution of L levels
Spatial resolution determined by sampling• Smallest discernible detail in an image
Gray-level resolution determined by number of gray scales• Smallest change in gray level
Spatial and Gray-LevelResolution
Down-sampling
Multi-rate image processing
•Up-sampling
Down-sampling operations
See the information loss due to downsampling
Gray-Level Reduction
Gray-Level Reduction
2k-level digital image of size NxN How K and N affect the image quality
Empirical study of resolutions
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
Sampling and quantizationQuality
Operations applied to digital images:
Zoom: up-sampling• Pixel duplication• Bi-linear interpolation
Shrink: down-sampling
Zoom and Shrink