Image Processing
This course covers the investigation, creation and manipulation of digital images by computer. The course consists of theoretical material introducing the mathematics of images and imaging. Topics include representation of two-dimensional data, time and frequency domain representations, filtering and enhancement, the Fourier transform, convolution, interpolation. The student will become familiar with Image Enhancement, Image Restoration, Image Compression, Morphological Image Processing, Image Segmentation, Representation and Description, and Object Recognition.
The objective of this course is to make students able to: develop a theoretical foundation of Digital Image Processing concepts; provide mathematical foundations for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression; gain experience and practical techniques to write programs for digital manipulation of images; image acquisition; pre-processing; segmentation; Fourier domain processing; and compression.
Course Contents
Definition of digital image, pixels, representation of digital image in spatial domain as well as in matrix form, Block diagram of fundamentals steps in digital image processing, Application of digital image processing system, Elements of Digital Image Processing systems, Structure of the Human Eye, Image Formation in the Eye, Brightness Adaptation and Discrimination, Basic Concepts in Sampling and Quantization, Representing Digital Images, Spatial and Gray-Level Resolution, Neighbors of a Pixel, Adjacency, Connectivity, Regions, and Boundaries, Distance Measures between pixels
Point operations, Contrast stretching, clipping and thresholding, digital negative, Intensity level slicing, log transformation, power log transformation, bit plane slicing, Unnormalized and Normalized Histogram, Histogram Equalization, Use of Histogram Statistics for Image Enhancement, Basics of Spatial Filtering, Linear filters, Spatial Low pass smoothing filters, Averaging, Weighted Averaging, Non-Linear filters, Median filter, Maximum and Minimum filters, High pass sharpening filters, High boost filter, high frequency emphasis filter, Gradient based filters, Robert Cross Gradient Operators, Prewitt filters, Sobel filters, Second Derivative filters, Laplacian filters, Magnification by replication and interpolation
Introduction to Fourier Transform and the frequency Domain, 1-D and 2-D Continuous and Discrete Fourier transform, Properties: Logarithmic, Separability, Translation, Periodicity, Ideal, Butterworth, and Gaussian Low Pass Filters, Ideal, Butterworth, and Gaussian High Pass Filters, Laplacian Filter, Computing and Visualizing the 2D DFT, Derivation of 1-D FFT, Time Complexity of DFT and FFT, Concept of Convolution, Correlation and Padding, Hadamard transform, Haar transform and Discrete Cosine transform
Models for Image degradation and restoration process, Noise Models (Gaussian, Rayleigh, Erlang, Exponential, Uniform and Impulse), Estimation of Noise Parameters, Mean Filters (Arithmetic, Geometric, Harmonic and Contraharmonic), Order Statistics Filters (Median, Min, Max, Midpoint, Alpha trimmed), Band pass and Band Reject filters (Ideal, Butterworth, Gaussian), Compression Ratio, Relative Data Redundancy, Average Length of Code, Coding Redundancy (Huffman), Interpixel Redundancy (Run Length), Psychovisual Redundancy (IGS Coding), Lossless and Lossy Predictive Model
Logic Operations involving binary images, Definition of Fit and Hit, Dilation and Erosion, Opening and Closing
Similarity and Discontinuity based techniques, Point, Line, and Edge Detection, Mexican Hat Filters, Edge Linking and Boundary Detection, Hough Transform, Thresholding: Global, Local and Adaptive, Region Based Segmentation: Region Growing, Region Split and Merge
Laboratory Works
Students are required to develop programs in related topics using suitable programming languages such as MatLab or Python or other similar programming languages.