Python for Signal and Image Processing Master Class

About Python for Signal and Image Processing Master Class

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