Digital Image Processing (Master’s Level)

This course provides a comprehensive introduction to the fundamental concepts and techniques of digital image processing, with a focus on theory, implementation, and practical applications. Based on the widely acclaimed book "Digital Image Processing" by Gonzalez and Woods, the course covers key topics in image analysis, enhancement, restoration, and compression. Course Objectives:

To understand the fundamental principles of digital image processing and their mathematical foundations. To develop skills in implementing image processing algorithms for various applications. To analyze and evaluate the performance of image processing techniques in practical scenarios.

Topics Covered:

Introduction to Digital Images:

Image representation and components. Digital image acquisition and sampling. Image resolution and dynamic range.

Image Enhancement:

Spatial domain techniques: Point operations, histogram processing, and spatial filters. Frequency domain techniques: Fourier transform, frequency filtering, and image sharpening.

Image Restoration:

Noise models and their impact on images. Image de-noising techniques and filtering.

Color Image Processing:

Color spaces (RGB, HSV, CMY, etc.). Color image enhancement and segmentation.

Image Compression:

Compression standards (e.g., JPEG, PNG). Lossy and lossless compression techniques.

Image Segmentation:

Edge detection, region-based segmentation, and clustering methods. Thresholding and morphological operations.

Morphological Image Processing:

Erosion, dilation, opening, and closing operations. Morphological algorithms for object detection.

Applications of Image Processing:

Medical imaging, remote sensing, object recognition, and more.

Course Features:

Practical Assignments: Students will implement image processing techniques using programming languages like Python or MATLAB, applying them to real-world datasets. Research-Oriented Learning: Opportunities to explore cutting-edge research topics in digital image processing. Critical Analysis: Students will evaluate the performance of various algorithms and propose improvements.

Target Audience:

This course is designed for master's students with a background in mathematics, computer science, or related fields. Prior knowledge of linear algebra, calculus, and basic programming is recommended.