The document covers the fundamentals of digital image processing, including key components, applications, and mathematical principles involved in the field. It highlights the differences between digital and analog image processing, discusses visual perception, and explains key concepts like sampling and quantization, as well as color models such as RGB and HSI. Additionally, the uses of digital image processing in various industries, such as medical imaging and machine vision, are summarized.

1. The Fundamentals of Digital Image
Processing
22/6/2020 - 26/2/2020
Day 5 : Applications
Summary :
•Steps in Digital Image Processing Components
•Elements of Visual Perception
•Image Sensing and Acquisition
•Image Sampling and Quantization
•Relationships between pixels
•Color image fundamentals - RGB, HSI models,
• Two-dimensional mathematical preliminaries, 2D
transforms - DFT, DCT.

2. Introduction
 Digital image processing is the use of computer
algorithms to perform image processing on digital
images
 As a subcategory or field of digital signal processing,
digital image processing has many advantages
over analog image processing.
 Since images are defined over two dimensions
(perhaps more) digital image processing may be
modeled in the form of multidimensional systems

3. Fundamental Systems
 Image Processing Systems

8. Components of an Image Processing System

9. Working Principles
VIDICON
The vidicon is a storage-type camera tube in which a charge-density pattern is
formed by the imaged scene radiation on a photoconductive surface which is then
scanned by a beam of low-velocity electrons. The fluctuating voltage coupled out
to a video amplifier can be used to reproduce the scene being imaged

10. Working Principles
Digital Camera
Digital and film cameras share an optical system, typically using a lens with a
variable diaphragm to focus light onto an image pickup device.The diaphragm
and shutter admit the correct amount of light to the imager, just as with film but
the image pickup device is electronic rather than chemical.
Most current consumer digital cameras use a Bayer filter mosaic in combination
with an optical anti-aliasing filter to reduce the aliasing due to the reduced
sampling of the different primary-color image

11. Working Principles
 Digital Camera

12. Elements of Visual Perception
Visual Perception
Visual perception is the ability to interpret the surrounding environment by
processing information that is contained in visible light. The resulting perception
is also known as eyesight, sight, or vision

13. Mach Band Effect
 The Mach bands effect is due to the spatial high-boost filtering performed by
the human visual system on the luminance channel of the image captured by
the retina. This filtering is largely performed in the retina itself, by lateral
inhibition among its neurons.

14. Colour Models
RGB Model
The RGB color model is an additive color model in which red, green, and blue
light are added together in various ways to reproduce a broad array of colors.
The main purpose of the RGB color model is for the sensing, representation, and
display of images in electronic systems, such as televisions and computers,
though it has also been used in conventional photography

15. Colour Models
 RGB Model

16. Colour Models
HSI Model
HSI, common in computer vision applications,
attempts to balance the advantages and disadvantages of the
other two systems HSL & HSV.

17. Sampling & Quantization
Sampling
 sampling is the reduction of a continuous signal to a discrete signal.
 A sample is a value or set of values at a point in time and/or space.
 A sampler is a subsystem or operation that extracts samples from a continuous
signal.
Quantization
 Quantization, involved in image processing, is a lossy compression technique
achieved by compressing a range of values to a single quantum value.
 When the number of discrete symbols in a given stream is reduced, the stream
becomes more compressible.

18. Sampling & Quantization
Sampling Quantization

19. Two Dimensional Mathematical
Preliminaries
Image Transforms
Many times, image processing tasks are best performed in a domain other than
the spatial domain.
Key steps:
(1) Transform the image
(2)Carry the task(s) in the transformed domain.
(3)Apply inverse transform to return to the spatial domain

20. Fourier Series Theorem
 Any periodic function f(t) can beexpressed as aweighted sum (infinite) of sine
and cosine functions of varying frequency
is called the “fundamentalfrequency

21. Discrete Fourier
Transform (DFT)

22. Discrete Fourier
Transform (DFT)
 Forward DFT
 InverseDFT

23. Discrete Cosine Transform A discrete cosine transform (DCT) expresses a finite sequence of data
points in terms of a sum of cosine functions oscillating at different frequencies.
 DCT is a Fourier-related transform similar to the discrete Fourier transform
(DFT), but using only real numbers. DCTs are equivalent to DFTs of roughly
twice the length, operating on real data with even symmetry. Types of DCT
listed below with 11 samples.

24. Applications of Digital Image Processing
Some of the major fields in which digital image processing is widely
used are mentioned below :
•Image sharpening and restoration
•Medical field
•Remote sensing
•Transmission and encoding
•Machine/Robot vision
•Colour processing
•Pattern recognition
•Video processing
•Microscopic Imaging
•Others

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