IMAGE PROCESSING

Course Overview
The Image Processing course provides comprehensive knowledge of techniques used for analyzing and manipulating digital images. This course is ideal for students and professionals in engineering, computer science, and related fields who wish to understand image processing concepts and apply them in real-world scenarios.

Learning Objectives
•    Understand the fundamentals of digital image processing.
•    Learn image acquisition, sampling, and quantization techniques.
•    Explore spatial and frequency domain processing.
•    Develop skills in image enhancement, restoration, and segmentation.
•    Implement image analysis and feature extraction methods.
•    Apply image processing algorithms using programming tools like MATLAB or Python.

Course Content

•    Introduction to Image Processing
•    What is Image Processing?
•    Applications of Image Processing
•    Overview of Digital Image Processing Systems
•    Image Acquisition and Hardware Tools

Digital Image Fundamentals
•    Image Representation
•    Sampling and Quantization
•    Basic Relationships Between Pixels
•    Color Models: RGB, HSV, and YCbCr

Image Enhancement Techniques
•    Enhancement in the Spatial Domain:
•    Point Processing (Contrast Stretching, Thresholding, Log Transformation)
•    Histogram Processing (Equalization, Specification)
•    Enhancement in the Frequency Domain:
•    Fourier Transform Basics
•    Filtering (Low Pass, High Pass, Band Pass)
•    Image Restoration

Introduction to Image Degradation
•    Noise Models
•    Restoration Techniques:
•    Inverse Filtering
•    Wiener Filtering
•    Regularized Filtering

Morphological Image Processing
•    Erosion and Dilation
•    Opening and Closing Operations
•    Morphological Algorithms for Shape Extraction

Image Segmentation

•    Thresholding Methods:
•    Global and Adaptive Thresholding
•    Edge Detection Techniques:
•    Sobel, Prewitt, and Canny Edge Detectors
•    Region-Based Segmentation
•    Watershed Segmentation

Feature Extraction and Object Recognition
•    Feature Descriptors (Corners, Edges, and Textures)
•    Hough Transform for Shape Detection
•    Principal Component Analysis (PCA)
•    Machine Learning Basics for Image Classification

Advanced Topics
•    Image Compression Techniques:
•    Lossy vs. Lossless Compression
•    JPEG, PNG, and TIFF Standards
•    Video Processing Basics
•    Introduction to Neural Networks for Image Processing
•    Programming Tools
•    MATLAB for Image Processing
•    Python Libraries (OpenCV, PIL, NumPy, and scikit-image)
•    Hands-on Coding Exercises

Final Project
•    Students will apply the techniques learned to develop a comprehensive image processing solution. Potential projects include:
•    Object Detection and Tracking
•    Medical Image Analysis
•    Image Restoration and Enhancement
•    Real-Time Image Processing for Applications

Course Deliverables
•    Detailed course materials with lecture notes and assignments.
•    Hands-on practice with real-world datasets.
•    Access to pre-written code and algorithms for experimentation.
•    Certificate of Completion upon successful project submission.