Python for Signal and Image Processing Master Class from Udemy

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

What's inside

Syllabus

Introduction of the Course

Pace of the Lecture Delivery

Course Material

Python Crash Course

Introduction of the Section

Python Installment

Installing Python Packages

Introduction of Jupyter Notebook

Arithmetic Operations Part01

Arithmetic Operations Part02

Arithmetic Operations Part03

Dealing With Arrays Part01

Dealing With Arrays Part02

Dealing With Arrays Part03

Plotting and Visualization Part01

Plotting and Visualization Part02

Plotting and Visualization Part03

Plotting and Visualization Part04

Lists in Python

For Loop Part01

For Loop Part02

Fundamentals of Signal Processing

Removing Spiky Noise With Median Filter in Python Part01

Basic Elements of Signal Processing

AD Conversion

AD Conversion With Python

Coding the Quantized Signal

Fundamentals of Continuous time signals

Continuous time signals in Python

Fundamentals of Discrete time signals

Discrete time signals in python

Sampling and Reconstruction

Sampling and Reconstruction in Python

The Convolution

The Convolution Sum

Numerical Example on Convolution

Full mode convolution

Convolution Using For Loop in Python

Convolution Using Numpy

Signal Denoising by Convolution

Edge Detection by Convolution

The Convolution Theorem

Signal Denoising

Signal Denoising by Moving Average Filter

Implementing Moving Average Filter in Python

Gaussian Mean Filter

Gaussian Mean Filter With Python

Median Filter

Median Filter in Python

Removing Spiky Noise With Median Filter

Removing Spiky Noise With Median Filter in Python Part02

Complex Number Systems

Introduction of Complex Numbers

Complex Numbers in Python

Mathematical Operations Part01

Mathematical Operations Part02

Mathematical Operations in Python

Magnitude and Phase Calculations

Magnitude and Phase Calculations in Python

Complex Sine Wave

Complex Sine Wave in Python

Fourier Transform

Combining Sine and Cosine Wave

Generating Waves in Python

Mechanism of Fourier Transform

Step by Step Coding of Fourier Transform

Fast Fourier Transform

Fourier Transform of Signal With DC Component

Amplitude and Power Spectrum

Inverse Fourier Transform

Application of Fourier Transform Part01

Application of Fourier Transform Part02

FIR Filter Design

Introduction of Digital Filters

Steps of Designing FIR Filters

FIR Filter Design by Least Square Method

FIR Filter Design by Window Method

FIR Zero Shift Filter

Low Pass FIR Filter

Low Pass FIR Filter in Python

High Pass FIR Filter

High Pass FIR Filter in Python

Band Pass FIR Filter

Band Pass FIR Filter in Python

Task for Students

IIR Filter Design

Introduction of IIR Filter

IIR Butterworth Filter Design in Python

Low Pass IIR Filter

High Pass IIR Filter

Band Pass IIR Filter

Comparison Between FIR and IIR Filters

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python for Signal and Image Processing Master Class with these activities:

Review Linear Algebra Fundamentals

Show steps

Strengthen your understanding of linear algebra concepts, which are fundamental to signal and image processing, especially in areas like Fourier transforms and image filtering.

Browse courses on Linear Algebra

Show steps

Review key concepts like vectors, matrices, and linear transformations.
Practice solving linear equation systems.
Work through examples of eigenvalue and eigenvector calculations.

Study 'The Scientist and Engineer's Guide to Digital Signal Processing' by Steven W. Smith

Show steps

Use this guide to reinforce your understanding of DSP concepts with a practical, application-oriented approach.

View Digital Signal Processing: A Practical Guide... on Amazon

Show steps

Read chapters on topics that you find challenging in the course.
Implement the algorithms described in the book using Python.
Compare the book's explanations with the course lectures.

Read 'Digital Signal Processing: Principles, Algorithms, and Applications' by Proakis and Manolakis

Show steps

Supplement your learning with a comprehensive textbook on digital signal processing to gain a deeper understanding of the underlying principles and algorithms.

View Digital Signal Processing on Amazon

Show steps

Read chapters related to Fourier transforms, filter design, and signal analysis.
Work through the examples and exercises provided in the book.
Compare the book's explanations with the course material for better comprehension.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Implement Convolution Algorithms from Scratch

Show steps

Solidify your understanding of convolution by implementing it from scratch using Python and NumPy, without relying on built-in functions.

Show steps

Write a Python function to perform convolution using nested loops.
Compare your implementation's output with NumPy's `convolve` function.
Test your function with different signals and kernels.

Create a Blog Post on Image Filtering Techniques

Show steps

Reinforce your knowledge of image filtering by writing a blog post explaining different filtering techniques and their applications.

Show steps

Choose a few image filtering techniques (e.g., Gaussian, median, Sobel).
Explain the theory behind each technique and provide Python code examples.
Include sample images to demonstrate the effects of each filter.
Publish your blog post on a platform like Medium or your personal website.

Prepare a Presentation on Fourier Transform Applications

Show steps

Deepen your understanding of the Fourier transform by creating a presentation that showcases its diverse applications in signal and image processing.

Show steps

Research different applications of the Fourier transform (e.g., audio processing, image compression, medical imaging).
Create slides explaining the theory behind the Fourier transform and how it is applied in each application.
Include visualizations and code examples to illustrate the concepts.
Practice your presentation and deliver it to your peers or colleagues.

Develop an Image Denoising Application

Show steps

Apply your knowledge of signal and image processing to build a practical application that denoises images using various filtering techniques.

Show steps

Implement several denoising filters (e.g., moving average, Gaussian, median, wavelet).
Create a user interface to upload images and select denoising parameters.
Evaluate the performance of each filter using metrics like PSNR or SSIM.
Document your project and share it on GitHub.

Career center

Learners who complete Python for Signal and Image Processing Master Class will develop knowledge and skills that may be useful to these careers:

Signal Processing Engineer

A signal processing engineer designs, develops, and tests signal processing systems. This course helps build skills relevant to a signal processing engineer by exploring both the theoretical foundations and practical Python implementations of signal processing algorithms. The course delves into topics such as convolution, Fourier transforms, filter design using FIR and IIR methods, and wavelet transforms. The comprehensive approach, covering everything from basic signal processing elements to advanced filter design, is particularly good for those who want to understand the engineering of signal processing systems. A signal processing engineer will find this course useful.

See salaries and explore the career path for Signal Processing Engineer

Computer Vision Engineer

A computer vision engineer develops algorithms that enable computers to 'see' and interpret images. This course provides a strong foundation for computer vision applications. The course will help a prospective computer vision engineer by covering both signal and image processing techniques using Python. The course's image processing sections, including working with NumPy, Matplotlib, and OpenCV, are directly related to common tasks in computer vision. Topics such as geometric operations, histogram processing, and spatial domain filtering are also covered. The course will also help one preparing to engineer novel computer vision applications.

See salaries and explore the career path for Computer Vision Engineer

Radar Systems Engineer

A radar systems engineer designs and develops radar systems. This course is directly applicable to understanding and implementing signal processing techniques used in radar. The radar systems engineer can use the course to explore topics like signal denoising, filter design, and Fourier transforms, all very useful for radar signal processing. The course's focus on Python implementation facilitates the application of these techniques in practical radar system development. This course can help a radar systems engineer.

See salaries and explore the career path for Radar Systems Engineer

Image Analyst

An image analyst extracts meaningful information from digital images. This course may be useful for those seeking to become image analysts because it provides a master class in Python for signal and image processing. The course covers fundamental image processing techniques using NumPy, Matplotlib, and OpenCV. Further, the course includes practical applications such as arithmetic and logic operations, geometric operations, and spatial and frequency domain filtering. The course will provide a solid foundation for analyzing and interpreting images. This makes it easier for an image analyst to process images in fields such as remote sensing, medical imaging, and computer vision.

See salaries and explore the career path for Image Analyst

Machine Learning Engineer

A machine learning engineer develops and implements machine learning algorithms. This course builds a foundation for machine learning engineering by delving into signal and image processing, which are vital for feature extraction and data analysis. The course helps advance skills by demonstrating how to implement algorithms in Python. The exploration of Fourier transforms and wavelet transforms also prepares the machine learning engineer to analyze complex datasets. Machine learning engineers will benefit from the course material as machine learning increasingly relies on processing complex signal and image data.

See salaries and explore the career path for Machine Learning Engineer

Deep Learning Engineer

A deep learning engineer designs and implements deep learning models. This course builds necessary image processing and signal processing skills. The course can help the deep learning engineer perform feature extraction and data analysis. It is also good to learn how to implement these algorithms in Python. Deep learning engineers will find the exploration of Fourier transforms and wavelet transforms useful as deep learning becomes more widespread in processing signal and image data.

See salaries and explore the career path for Deep Learning Engineer

Remote Sensing Analyst

A remote sensing analyst interprets data collected from satellites and aircraft. This course enables a candidate to seek out a role as a remote sensing analyst. It does this by covering image processing techniques essential for analyzing remote sensing data. The course's curriculum includes practical applications using Python, NumPy, and OpenCV, directly applicable to processing remote sensing imagery. Specific topics such as spatial and frequency domain filtering provide the skills remote sensing analyst need to extract information from images of the Earth's surface.

See salaries and explore the career path for Remote Sensing Analyst

Robotics Engineer

A robotics engineer designs, builds, and programs robots. The curriculum of this course will benefit robotics engineers, offering a strong foundation in image processing and signal processing. These are essential for robot vision and sensor data analysis. The robotics engineer will also learn how to implement image and signal processing algorithms in Python. Specific sections like those covering OpenCV and geometric operations significantly contribute to skills used in robotics development. Taking this course helps a robotics engineer learn to teach robots to 'see'.

See salaries and explore the career path for Robotics Engineer

Acoustic Engineer

An acoustic engineer studies and manipulates sound and vibration. This course will help those who are studying for this kind of engineering profession. This course helps build a strong foundation in signal processing techniques, essential for acoustic analysis and design. The curriculum of this course also covers signal denoising, filter design, and Fourier transforms. This allows a candidate to be prepared for acoustic signal processing. An acoustic engineer will find this course useful.

See salaries and explore the career path for Acoustic Engineer

Medical Imaging Specialist

A medical imaging specialist works with medical imaging technologies like MRI and X-ray. This course helps those preparing for a role as a medical imaging specialist. The course covers image processing techniques and their implementation in Python. Medical imaging specialists acquire and process images and should have an understanding of methods like Fourier and wavelet transforms. The course's focus on practical applications using NumPy, Matplotlib, and OpenCV makes a good fit for an aspiring medical imaging specialist.

See salaries and explore the career path for Medical Imaging Specialist

Geospatial Analyst

A geospatial analyst works with geographic data and technologies to analyze spatial information. This course may be useful for a geospatial analyst because it helps them acquire image processing skills for analyzing satellite and aerial imagery. The course covers practical applications using Python, NumPy, and OpenCV. This allows a geospatial analyst to implement image processing techniques. The geospatial analyst will be able to perform spatial analysis and extract insights from geographic data.

See salaries and explore the career path for Geospatial Analyst

Biomedical Engineer

A biomedical engineer applies engineering principles to solve medical and healthcare-related problems. This course may be useful for biomedical engineers by introducing them to skills in signal and image processing. This course is relevant to biomedical engineers, who benefit by developing expertise for analyzing medical signals and images, such as EEGs and MRIs. The biomedical engineer will find that the coverage of Fourier transforms, wavelet transforms, and image processing techniques helps in medical data analysis.

See salaries and explore the career path for Biomedical Engineer

Automation Engineer

An automation engineer designs and implements automated systems. This course may be useful for automation engineers, as it helps them to develop skills in image processing and signal processing. These techniques are essential for tasks such as automated visual inspection and control systems. The curriculum of this course will allow the automation engineer to implement the algorithms in Python. Specific modules, like those that cover OpenCV, are beneficial to the automation engineer.

See salaries and explore the career path for Automation Engineer

Data Scientist

A data scientist analyzes large datasets to extract insights and inform decision making. This course may be useful because it helps the data scientist acquire skills in data analysis, signal processing, and image processing using Python. The Python focus is also useful for data scientists. The course covers various topics such as Fourier transforms, wavelet transforms, and statistical signal processing techniques. These help build the quantitative skills necessary for a data scientist role. A data scientist should consider taking this course.

See salaries and explore the career path for Data Scientist

Financial Analyst

A financial analyst analyzes financial data to provide investment recommendations. This course may be useful because it helps the financial analyst acquire skills in signal processing and time series analysis using Python. The course explores time series analysis and covers Fourier transforms, which are applicable to financial data. In addition to time series analysis, the course helps build the quantitative skills necessary for a financial analyst role. It also covers probability, statistics, and decision theory.

See salaries and explore the career path for Financial Analyst

Python for Signal and Image Processing Master Class

What's inside

Syllabus

Save this course

Activities

Career center

Reading list

Share

Similar courses