Course Contents:
Unit 1: Introduction : Digital Image Processing 4 hours
Digital image representation, Digital image processing: Problems and applications, Elements of visual perception, Sampling and quantization, Some basic relationships like Neighbors, Connectivity, Distance, Measures between pixels, Visual Perception
Unit 2: Image Enhancement in Spatial Domain 4 hours
Gray Level Transformations, Histogram Processing, Enhancement Using Arithmetic and Logic operations, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters, Combining Spatial Enhancement Methods
Unit 3: Image Enhancement in the Frequency Domain 5 Hours
Introduction to Fourier Transform and the frequency Domain, Computing and Visualizing the 2D DFT, Smoothing and Sharpening using Frequency Domain Filters, Hadamard transform, Haar transform and Discrete Cosine transform, Fast Fourier Transform
Unit 4: Image Restoration 7 Hours
The Image Degradation / Restoration Process, Noise Model based Restoration, Spatial filtering, Periodic Noise Reduction by Frequency Domain Filtering, Inverse filtering, Wiener filtering, Geometric Mean Filter
Unit 5 : Color Processing 5 Hours
Color Fundamentals, Color Models, Pseudocolor based Image Processing, Color transformations, Smoothing and Sharpening operations
Unit 6: Image Compression 5 Hours
Coding, Interpixel and Psychovisual Redundancy, Image Compression models, Lossless and Lossy Compressions
Unit 7: Morphological Image Processing 5 Hours
Logic Operations involving binary images, Dilation and Erosion, Opening and Closing, The Hit-or-Miss Transformation
Unit 8: Image Segmentation 5 Hours
Detection of Discontinuities, Edge linking and boundary detection, Thresholding, Region Based Segmentation
Unit 9: Pattern Recognition 8 Hours
Descriptor concept, Chain codes, Signatures, Shape Numbers, Fourier Descriptors, Patterns and pattern classes, Overview of pattern recognition, Neural Network and Image Processing, NN based pattern recognition, Decision-Theoretic Pattern Recognition Methods.
Lab and Project Requirement:
This course requires extensive exposure of practical examples with at least 8-12 lab exercises with programs consisting most of topics detailed in syllabus content. Semester end, image-processing project as a course project (either individual or group (at most 4 students) is a strict requirement for this course.
