Python For Signal and Image Processing Master Class

Signal and Image Processing Algorithms : Theory, Intuition, Mathematics, Numerical examples, and Python Implementation

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Last Updated: 2023-12-15 09:12:23 English Course Type: Self Learning

Fundamentals of Signals and Image Processing.
Analog to digital conversion.
Sampling and Reconstruction.
Nyquist Theorem.
Convolution for Signal and Images.
Signal and Image denoising.
Fourier transform of Signals and Images.
Signal filtering by FIR and IIR filters.
Image Filtering in Spatial and Frequency Domain
Wavelet Transform for Signal and Images.
Histogram Processing
Arithmetic, Logic and Point Level Operations on Images
implementation of all Signal and Image Processing Algorithms in Python
Google Colab for Python Programming

This course will bridge the gap between the theory and implementation of Signal and Image Processing Algorithms and their implementation in Python. All the lecture slides and python codes are provided.

Why Signal Processing?

Since the availability of digital computers in the 1970s, digital signal processing has found its way in all sections of engineering and sciences.

Signal processing is the manipulation of the basic nature of a signal to get the desired shaping of the signal at the output. It is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals.

Following areas of sciences and engineering are specially benefitted by rapid growth and advancement in signal processing techniques.

1. Machine Learning.

2. Data Analysis.

3. Computer Vision.

4. Image Processing

5. Communication Systems.

6. Power Electronics.

7. Probability and Statistics.

8. Time Series Analysis.

9. Finance

10. Decision Theory

Why Image Processing?

Image Processing has found its applications in numerous fields of Engineering and Sciences.

Few of them are the following.

1. Deep Learning

2. Computer Vision

3. Medical Imaging

4. Radar Engineering

5. Robotics

6. Computer Graphics

7. Face detection

8. Remote Sensing

9. Agriculture and food industry

Course Outline

Section 01: Introduction of the course

Section 02: Python crash course

Section 03: Fundamentals of Signal Processing

Section 04: Convolution

Section 05: Signal Denoising

Section 06: Complex Numbers

Section 07: Fourier Transform

Section 08: FIR Filter Design

Section 09: IIR Filter Design

Section 10: Introduction to Google Colab

Section 11: Wavelet Transform of a Signal

Section 12: Fundamentals of Image Processing

Section 13: Fundamentals of Image Processing With NumPy and Matplotlib

Section 14: Fundamentals of Image Processing with OpenCV

Section 15: Arithmetic and Logic Operations with Images

Section 16: Geometric Operations with Images

Section 17: Point Level OR Gray level Transformation

Section 18: Histogram Processing

Section 19: Spatial Domain Filtering

Section 20: Frequency Domain Filtering

Section 21: Morphological Processing

Section 22: Wavelet Transform of Images

Gmail Account ( For Google Colab )

Anyone who wants to learn Signal and Image Processing from scratch using Python.
Anyone who wants to work in Signal and Image Processing area.
Those students who know the Maths of Signal and Image Processing but don't know how to implement with Python.
Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.
Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.

Section 1 : Introduction 2 Lectures 00:05:52
Lecture 1 :
Introduction of the Course Preview
Lecture 2 :
Course Material
Section 2 : Python Crash Course 17 Lectures 03:29:19
Lecture 1 :
Introduction of the Section
Lecture 2 :
Installing Python Packages- Part01
Lecture 3 :
Installing Python Packages- Part02
Lecture 4 :
Introduction of Jupyter Notebook
Lecture 5 :
Arithmetic With Python Part-01
Lecture 6 :
Arithmetic With Python Part-02
Lecture 7 :
Arithmetic With Python Part-03
Lecture 8 :
Dealing With Arrays Part-01
Lecture 9 :
Dealing With Arrays Part-02
Lecture 10 :
Dealing With Arrays Part-03
Lecture 11 :
Plotting and Visualization Part-01
Lecture 12 :
Plotting and Visualization Part-02
Lecture 13 :
Plotting and Visualization Part-03
Lecture 14 :
Plotting and Visualization Part-04
Lecture 15 :
Lists In Python
Lecture 16 :
For Loops Part-01
Lecture 17 :
For Loops Part-02
Section 3 : Fundamentals of Signal Processing 11 Lectures 02:07:07
Lecture 1 :
Introduction of the Section
Lecture 2 :
Basic Elements of Signal Processing System
Lecture 3 :
Analog to Digital Conversion
Lecture 4 :
Analog to Digital Conversion With Python
Lecture 5 :
Coding the Quantized Signal
Lecture 6 :
Fundamentals of Continuous Time Signal
Lecture 7 :
Continuous Time Signals In Python
Lecture 8 :
Fundamentals of Discrete Time Signal
Lecture 9 :
Discrete Time Signals In Python
Lecture 10 :
Sampling and Reconstruction
Lecture 11 :
Sampling and Reconstruction in Python
Section 4 : Convolution 9 Lectures 01:37:41
Lecture 1 :
Introduction of the Section
Lecture 2 :
The Convolution Sum
Lecture 3 :
Numerical Example on Convolution
Lecture 4 :
Convolution Using For Loops In Python
Lecture 5 :
Convolution Using NumPy
Lecture 6 :
Signal Denoising By Convolution
Lecture 7 :
Edge Detection By Convolution
Lecture 8 :
The Convolution Theorem
Lecture 9 :
Full Mode Convolution
Section 5 : Signal Denoising 10 Lectures 01:40:35
Lecture 1 :
Introduction of the Section
Lecture 2 :
Signal Denoising By Moving Average Filter
Lecture 3 :
Implementing Moving Average Filter in Python
Lecture 4 :
Gaussian Mean Filter
Lecture 5 :
Gaussian Mean Filter In Python
Lecture 6 :
Median Filter
Lecture 7 :
Median Filter In Python
Lecture 8 :
Removing Spiky Noise By Median Filter
Lecture 9 :
Removing Spiky Noise By Median Filter in Python Part-01
Lecture 10 :
Removing Spiky Noise By Median Filter in Python Part-02
Section 6 : Complex Number System 9 Lectures 00:30:42
Lecture 1 :
Introduction of Complex Number
Lecture 2 :
Complex Numbers in Python
Lecture 3 :
Mathematical Operations Part-01
Lecture 4 :
Mathematical Operations Part-02
Lecture 5 :
Mathematical Operations in Python
Lecture 6 :
Magnitude and Phase Calculation
Lecture 7 :
Magnitude and Phase Calculation In Python
Lecture 8 :
Complex Sine Wave
Lecture 9 :
Complex Sine Wave In Python
Section 7 : Fourier Transform 11 Lectures 01:58:30
Lecture 1 :
Introduction of the Section
Lecture 2 :
Combining Sine and Cosine Wave
Lecture 3 :
Generating Waves in Python
Lecture 4 :
Mechanism of Fourier Transform
Lecture 5 :
Step By Step Coding of Fourier Transform
Lecture 6 :
Fast Fourier Transform
Lecture 7 :
Fourier Transform of Signal with DC Component
Lecture 8 :
Amplitude and Power Spectrum
Lecture 9 :
Inverse Fourier Transform
Lecture 10 :
Application of Fourier Transform Part-01
Lecture 11 :
Application of Fourier Transform Part-02
Section 8 : Finite Impulse Response ( FIR ) Filter Design 13 Lectures 01:57:05
Lecture 1 :
Introduction of the Section
Lecture 2 :
Introduction of Digital Filters
Lecture 3 :
Steps of Designing FIR Filters
Lecture 4 :
FIR Filter Design by Least Square Method
Lecture 5 :
FIR Filter Design by Window Method
Lecture 6 :
FIR Zero Shift Filter
Lecture 7 :
Low Pass FIR Filter
Lecture 8 :
Low Pass FIR Filter In Python
Lecture 9 :
High Pass FIR Filter
Lecture 10 :
High Pass FIR Filter In Python
Lecture 11 :
Band Pass Filter
Lecture 12 :
Band Pass Filter In Python
Lecture 13 :
Task for Students
Section 9 : Infinite Impulse Response ( IIR ) Filter Design 8 Lectures 00:44:36
Lecture 1 :
Introduction of the Section
Lecture 2 :
Introduction of IIR Filters
Lecture 3 :
IIR Butterworth Filter Design in Python
Lecture 4 :
Low Pass IIR Filter
Lecture 5 :
High Pass IIR Filter
Lecture 6 :
Band Pass IIR Filter
Lecture 7 :
Comparison Between FIR and IIR Filter
Lecture 8 :
Task for Students
Section 10 : Introduction to Google Colab 4 Lectures 00:25:37
Lecture 1 :
Introduction of the Section
Lecture 2 :
Python Coding in Colab Part-01
Lecture 3 :
Python Coding in Colab Part-02
Lecture 4 :
Python Coding in Colab Part-03
Section 11 : Wavelet Transform of Signals 11 Lectures 01:07:50
Lecture 1 :
Introduction of the Section
Lecture 2 :
Limitations of Fourier Transform
Lecture 3 :
Why Wavelet Transform
Lecture 4 :
Wavelet Families
Lecture 5 :
Filter Banks of Discrete Wavelets
Lecture 6 :
Single Level Decomposition
Lecture 7 :
Single Level Decomposition With Python
Lecture 8 :
Multilevel Decomposition
Lecture 9 :
Multilevel Decomposition In Python
Lecture 10 :
Time Frequency Analysis
Lecture 11 :
Time Frequency Analysis In Python
Section 12 : Fundamentals of Image Processing 4 Lectures 00:20:01
Lecture 1 :
Introduction of the Section
Lecture 2 :
Concept of an Image
Lecture 3 :
How Computers See the Image
Lecture 4 :
Digital Image Processing
Section 13 : Image Fundamentals With NumPy and Matplotlib 4 Lectures 00:29:47
Lecture 1 :
Introduction of the Section
Lecture 2 :
Reading, Displaying and Saving Image
Lecture 3 :
Image Formats
Lecture 4 :
Red, Green and Blue Components of Image
Section 14 : Image Fundamentals With OpenCV 3 Lectures 00:23:50
Lecture 1 :
Introduction of the Section
Lecture 2 :
Image Reading and Displaying
Lecture 3 :
Image Resizing and Flipping
Section 15 : Arithmetic and Logic Operations on Images 5 Lectures 00:47:37
Lecture 1 :
Introduction of the Section
Lecture 2 :
Arithmetic Operations
Lecture 3 :
Arithmetic Operations With Python
Lecture 4 :
Logical Operations
Lecture 5 :
Logical Operations With Python
Section 16 : Geometric Operations 4 Lectures 00:28:09
Lecture 1 :
Introduction of the Section
Lecture 2 :
Translation, Rotation and Affine Transformation
Lecture 3 :
Translation, Rotation and Affine Transformation With Python
Lecture 4 :
Scaling, Zooming, Shrinking and Cropping
Section 17 : Gray Level and Point Transformations 9 Lectures 00:32:33
Lecture 1 :
Introduction of the Section
Lecture 2 :
Negative Point Transformation
Lecture 3 :
Negative Point Transformation With Python
Lecture 4 :
Log Transformation
Lecture 5 :
Log Transformation With Python
Lecture 6 :
Gamma Transformation
Lecture 7 :
Gamma Transformation With Python
Lecture 8 :
Auto-contrast and Piece-wise linear contrast function
Lecture 9 :
Contrast Functions With Python
Section 18 : Histogram Processing 6 Lectures 00:56:56
Lecture 1 :
Introduction of the Section
Lecture 2 :
Histogram of an Image
Lecture 3 :
Histogram of Image With Python Part-01
Lecture 4 :
Histogram of Image With Python Part-02
Lecture 5 :
Histogram Equalization With Numerical Example
Lecture 6 :
Histogram Equalization With Python
Section 19 : Spatial Domain Filtering 20 Lectures 01:39:52
Lecture 1 :
Introduction of the Section
Lecture 2 :
Neighborhood Processing
Lecture 3 :
2D Convolution With Numerical Example
Lecture 4 :
2D Convolution With Python
Lecture 5 :
Applications of 2D Convolution
Lecture 6 :
Applications of 2D Convolution With Python
Lecture 7 :
Mean Filter
Lecture 8 :
Mean Filter With Python
Lecture 9 :
Gaussian Filter
Lecture 10 :
Gaussian Filter With Python
Lecture 11 :
Median Filter
Lecture 12 :
Median Filter With Python
Lecture 13 :
The Laplacian
Lecture 14 :
Laplacian Filtering With Python
Lecture 15 :
High boost filter
Lecture 16 :
High boost filter With Python
Lecture 17 :
Sobel Filters
Lecture 18 :
Sobel Filters in Python
Lecture 19 :
Canny Edge Detection
Lecture 20 :
Canny Edge Detection With Python
Section 20 : Frequency Domain Filtering 7 Lectures 00:34:35
Lecture 1 :
Introduction of the Section
Lecture 2 :
2D Fourier Transform
Lecture 3 :
2D Fourier Transform With Python
Lecture 4 :
Low Pass and High Pass Filters
Lecture 5 :
Low Pass and High Pass Filters With Python
Lecture 6 :
High Boost and Other Filters
Lecture 7 :
Fourier Transform of High Boost Filter
Section 21 : Morphological Processing 7 Lectures 00:26:45
Lecture 1 :
Introduction of the Section
Lecture 2 :
Dilation and Erosion
Lecture 3 :
Dilation and Erosion With Python
Lecture 4 :
Morphological Filtering
Lecture 5 :
Morphological Filtering With Python
Lecture 6 :
Image Gradient Using Morphology
Lecture 7 :
Morphological Gradient With Python
Section 22 : Wavelet Transform for Images 7 Lectures 00:29:06
Lecture 1 :
Introduction of the Section
Lecture 2 :
Single Level Decomposition and Reconstruction
Lecture 3 :
Single Level Decomposition and Reconstruction With Python
Lecture 4 :
Multilevel Decomposition and Reconstruction
Lecture 5 :
Multilevel Decomposition and Reconstruction With Python
Lecture 6 :
Image denoising using Wavelet Transform
Lecture 7 :
Image denoising using Wavelet Transform With Python

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Zeeshan Ahmad,

3412 Course Views

4 Courses

Dr. Zeeshan is PhD in Electrical and Computer Engineering from Toronto Metropolitan University. He has more than 18 years of teaching and research experience. He has taught many courses related to Computer Science and Computer Engineering. such as Machine Learning, Deep Learning, Image Processing, Computer Vision, Signal Processing and Python Programming. He has publications in reputed journals and conferences.