Introduction and Fundamentals: Motivation and Perspective, Applications, Components of Image Processing System, Element of Visual Perception, A Simple Image Model, Sampling and Quantization.
Image Enhancement in Spatial Domain: Introduction; Basic Gray Level Functions – Piecewise-Linear Transformation Functions: Contrast Stretching; Histogram Specification; Histogram Equalization; Local Enhancement; Enhancement using Arithmetic/Logic Operations – Image Subtraction, Image Averaging; Basics of Spatial Filtering; Smoothing - Mean filter, Ordered Statistic Filter; Sharpening – The Laplacian.

Image Enhancement in Frequency Domain: Fourier Transform and the Frequency Domain, Basis of Filtering in Frequency Domain, Filters – Low-pass, High-pass; Correspondence Between Filtering in Spatial and Frequency Domain; Smoothing Frequency Domain Filters – Gaussian Low-pass Filters; Sharpening Frequency Domain Filters – Gaussian High-pass Filters; Homomorphic Filtering.

Image Restoration: A Model of Restoration Process, Noise Models, Restoration in the presence of Noise only-Spatial Filtering – Mean Filters: Arithmetic Mean filter, Geometric Mean Filter, Order Statistic Filters – Median Filter, Max and Min filters; Periodic Noise Reduction by Frequency Domain Filtering– Band-pass Filters; Minimum Mean-square Error Restoration.

Color Image Processing: Color Fundamentals, Color Models, Converting Colors to different models, Color Transformation, Smoothing and Sharpening, Color Segmentation.
Morphological Image Processing: Introduction, Logic Operations involving Binary Images, Dilation and Erosion, Opening and Closing, Morphological Algorithms – Boundary Extraction, Region Filling, Extraction of Connected Components, Convex Hull, Thinning, Thickening.

Image Compression: Fundamentals, image compression models, Compression methods: Huffman coding, Golomb Coding, Arithmetic Coding, LZW coding, Run-Length coding, Symbol based coding.error-free compression, lossy predictive coding, image compression standards.
Image Segmentation:Fundamentals, Point, Line and edge detection. Thresholding: foundation, Basic Global Thresholding, Otsu’s Method, Image smoothing to improve global thresholding.

Introduction# and# Fundamentals:# Motivation# and# Perspective,# Applications,# Components# of# Image# Processing# System,# Element# of# Visual# Perception,# A# Simple# Image# Model,# Sampling# and# Quantization.
Image# Enhancement# in# Spatial# Domain:# Introduction;# Basic# Gray# Level# Functions# –# Piecewise-Linear# Transformation# Functions:# Contrast# Stretching;# Histogram# Specification;# Histogram# Equalization;# Local# Enhancement;# Enhancement# using# Arithmetic/Logic# Operations# –# Image# Subtraction,# Image# Averaging;# Basics# of# Spatial# Filtering;# Smoothing# -# Mean# filter,# Ordered# Statistic# Filter;# Sharpening# –# The# Laplacian.

Image# Enhancement# in# Frequency# Domain:# Fourier# Transform# and# the# Frequency# Domain,# Basis# of# Filtering# in# Frequency# Domain,# Filters# –# Low-pass,# High-pass;# Correspondence# Between# Filtering# in# Spatial# and# Frequency# Domain;# Smoothing# Frequency# Domain# Filters# –# Gaussian# Low-pass# Filters;# Sharpening# Frequency# Domain# Filters# –# Gaussian# High-pass# Filters;# Homomorphic# Filtering.

Image# Restoration:# A# Model# of# Restoration# Process,# Noise# Models,# Restoration# in# the# presence# of# Noise# only-Spatial# Filtering# –# Mean# Filters:# Arithmetic# Mean# filter,# Geometric# Mean# Filter,# Order# Statistic# Filters# –# Median# Filter,# Max# and# Min# filters;# Periodic# Noise# Reduction# by# Frequency# Domain# Filtering# Band-pass# Filters;# Minimum# Mean-square# Error# Restoration.

Color# Image# Processing:# Color# Fundamentals,# Color# Models,# Converting# Colors# to# different# models,# Color# Transformation,# Smoothing# and# Sharpening,# Color# Segmentation.
Morphological# Image# Processing:# Introduction,# Logic# Operations# involving# Binary# Images,# Dilation# and# Erosion,# Opening# and# Closing,# Morphological# Algorithms# –# Boundary# Extraction,# Region# Filling,# Extraction# of# Connected# Components,# Convex# Hull,# Thinning,# Thickening.#

Image# Compression:# Fundamentals,# image# compression# models,# Compression# methods:# Huffman# coding,# Golomb# Coding,# Arithmetic# Coding,# LZW# coding,# Run-Length# coding,# Symbol# based# coding.error-free# compression,# lossy# predictive# coding,# image# compression# standards.#
Image# Segmentation:Fundamentals,# Point,# Line# and# edge# detection.# Thresholding:# foundation,# Basic# Global# Thresholding,# Otsus# Method,# Image# smoothing# to# improve# global# thresholding.


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