Python for Signal and Image Processing Master Class [2023]

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

What you'll learn
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
Python Crash Course

Requirements
Basic Programming Skill will be an asset but not necessary. You will learn everything in this course.
Description
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

Who this course is for:
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.
Students and practitioners who know implementation of signal and image processing algorithms in MATLAB but want to switch to Python.

More Info