Optical image processing systems typically consist of several fundamental components, each playing a crucial role in capturing, processing, and analyzing images. Here are the key components: 1. **Imaging Sensor**: The imaging sensor is the core component responsible for capturing the optical image. It could be a CCD (Charge-Coupled Device) or CMOS (Complementary Metal-Oxide-Semiconductor) sensor. These sensors convert incoming light into electrical signals, which are then processed further. 2. **Lens System**: The lens system focuses incoming light onto the imaging sensor, ensuring clarity and sharpness in the captured image. Different lenses may be used for various purposes such as zooming, wide-angle capture, or macro photography. 3. **Illumination Source**: In many optical image processing systems, especially in applications like microscopy or machine vision, an illumination source is used to provide adequate lighting for the scene being captured. This could be ambient light, LED lights, halogen lamps, or specialized light sources depending on the requirements of the application. 4. **Image Processing Unit (IPU)**: This unit processes the raw data captured by the imaging sensor to enhance the image quality, perform various analyses, or extract relevant information. It typically involves algorithms for noise reduction, color correction, image enhancement, feature extraction, and pattern recognition. 5. **Processor/Computer**: The processed image data is often sent to a processor or a computer for further analysis and interpretation. This could involve running sophisticated algorithms for object recognition, image classification, segmentation, or any other specific tasks relevant to the application. 6. **Display Unit**: The results of the image processing are usually displayed on a monitor or some other visualization device. This allows operators or users to interpret the processed images and make decisions based on the analysis performed. 7. **Storage System**: Images or processed data may need to be stored for future reference, analysis, or archival purposes. A storage system, such as a hard drive or cloud storage, is used to save the data securely. 8. **Control Interface**: Many optical image processing systems feature a control interface that allows users to adjust settings, initiate image capture, or interact with the system. This could be a physical interface with buttons and knobs or a software-based interface. These components work together to capture, process, and analyze optical images for a wide range of applications, including medical imaging, surveillance, industrial inspection, remote sensing, and scientific research.

1. UNIT-I DIGITAL IMAGE FUNDAMENTALS
Department of ECE Engineering,
Velammal Institute of Technology
Presented by : K. Ragupathi
Designation : Assistant Professor
Course Code : CEC358
Course Title : Underground imaging
system and image
processing

2. Presentation Outline
• Introduction
• Image representation
• Elements of digital image processing systems

3. 28 –Feb -2024 Department of ECE, Velammal Institute of Technology 1
Introduction
What Is Digital Image Processing?
The field of digital image processing refers to processing digital images by means
of a digital computer.

4. 28 –Feb -2024 Department of ECE, Velammal Institute of Technology 1
Image Representation
What is a Digital Image ?
An image may be defined as a two- dimensional function, f(x,y) where x and y are spatial (plane)
coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray
level of the image at that point.
When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image
a digital image

5. 28 –Feb -2024 Department of ECE, Velammal Institute of Technology 1
Image Representation
Picture elements, Image elements, pels, and pixels
• A digital image is composed of a finite number of elements, each of which has a particular location
and value.
• These elements are referred to as picture elements, image elements, pels, and pixels.
• Pixel is the term most widely used to denote the elements of a digital image.

6. 28 –Feb -2024 Department of ECE, Velammal Institute of Technology 1
Image Representation
Samples = pixels
Quantization = number of bits per pixel
Example: if we would sample and quantize standard TV picture (525 lines) by using VGA
(Video Graphics Array), video controller creates matrix 640x480pixels, and each pixel is
represented by 8 bit integer (256 discrete gray levels)
Black and white image
single color plane with 2 bits
Grey scale image
single color plane with 8 bits
Color image
three color planes each with 8 bits
RGB, CMY, YIQ, etc.
Indexed color image
single plane that indexes a color table
Compressed images
TIFF, JPEG, BMP, etc.
4 gray levels 2 gray levels

7. 28 –Feb -2024 Department of Bio Medical Engineering, E.G.S Pillay Engineering College, Nagapattinam 1
Image Representation
Samples = pixels
Quantization = number of bits per pixel
Example: if we would sample and quantize standard TV picture (525 lines) by using VGA
(Video Graphics Array), video controller creates matrix 640x480pixels, and each pixel is
represented by 8 bit integer (256 discrete gray levels)
Black and white image
single color plane with 2 bits
Grey scale image
single color plane with 8 bits
Color image
three color planes each with 8 bits
RGB, CMY, YIQ, etc.
Indexed color image
single plane that indexes a color table
Compressed images
TIFF, JPEG, BMP, etc.
4 gray levels 2 gray levels

8. 28 –Feb -2024 Department of Bio Medical Engineering, E.G.S Pillay Engineering College, Nagapattinam 1
Image Representation
Digital Image Representation
(3 Bit Quantization)
Color Quantization
Example of 24 bit RGB Image

9. 28 –Feb -2024 Department of Bio Medical Engineering, E.G.S Pillay Engineering College, Nagapattinam 1
Elements of digital image processing systems

10. 20 –July -2022 Department of Bio Medical Engineering, E.G.S Pillay Engineering College, Nagapattinam 1
Elements of digital image processing systems
Step 1: Image Acquisition
 The image is captured by a sensor (eg. Camera), and digitized if the output of the
camera or sensor is not already in digital form, using analogue-to-digital
convertor.
Step 2: Image Enhancement
 The process of manipulating an image so that the result is more suitable than the
original for specific applications.
 The idea behind enhancement techniques is to bring out details that are hidden,
or simple to highlight certain features of interest in an image.

11. 20 –July -2022 Department of ECE, Velammal Institute of Technology 1
Elements of digital image processing systems
Step 3: Image Restoration
 Improving the appearance of an image
 Tend to be mathematical or probabilistic models. Enhancement, on the other
hand, is based on human subjective preferences regarding what constitutes a
“good” enhancement result.
Step 4: Color Image Processing
 Use the color of the image to extract features of interest in an image

12. 20 –July -2022 Department of ECE, Velammal Institute of Technology 1
Elements of digital image processing systems
Step 5: Wavelets
Are the foundation of representing images in various degrees of resolution. It is used
for image data compression.
Step 6: Compression
Techniques for reducing the storage required to save an image or the bandwidth
required to transmit it.

13. 28 –Feb -2024 Department of ECE, Velammal Institute of Technology 1
Elements of digital image processing systems
Step 7: Morphological Processing
 Tools for extracting image components that are useful in the representation and
description of shape.
 In this step, there would be a transition from processes that output images, to
processes that output image attributes.
Step 8: Image Segmentation
 Segmentation procedures partition an image into its constituent parts or objects.

14. 28 –Feb -2024 Department of ECE, Velammal Institute of Technology 1
Elements of digital image processing systems
Step 9: Representation and Description
 Representation: Make a decision whether the data should be represented as a
boundary or as a complete region. It is almost always follows the output of a
segmentation stage.
 Boundary Representation: Focus on external shape characteristics, such
as corners and inflections
 Region Representation: Focus on internal properties, such as texture or
skeleton
 Choosing a representation is only part of the solution for transforming raw data
into a form suitable for subsequent computer processing (mainly recognition)
 Description: also called, feature selection, deals with extracting attributes that
result in some information of interest.

15. THANK YOU