D igital Image Processing: Digital Imaging Fundamentals

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