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Digital Image Processing

This web-based training course on Digital Image Processing functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

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Course Details

This course helps trainees to learn and understand the basic principles and tools used to process images and videos. With this course trainees will learn origins of digital image processing, various steps used in digital image processing, image sampling and quantization, linear and nonlinear operations, gray level transformations, image enhancement in the frequency domain, sharpening frequency domain filters and periodic noise reduction by frequency domain filtering. This course educates trainees to learn color image processing, noise in color images, wavelet transforms in one dimension, image processing models and error free compression. Furthermore, trainees will learn edge linking and boundary detection, the use of motion in segmentation and objects recognition. There are no prerequisites for this course. This course is beneficial for both fresher and professionals to enhance their learning and skills.


  • What is Digital Image Processing?
  • Understanding the Origins of Digital Image Processing
  • Usage of Digital Image Processing
  • Various Steps in Digital Image Processing
  • Components of an Image Processing System

Digital Image Fundamentals

  • Understanding Elements of Visual Perception
  • Explaining Light and the Electromagnetic Spectrum
  • Describing Image Sensing and Acquisition
  • Understanding Image Sampling and Quantization
  • Basic Relationships Between Pixels
  • Explaining Linear and Nonlinear Operations

Image Enhancement in the Spatial Domain

  • Describing Basic Gray Level Transformations
  • Understanding Histogram Processing
  • Explaining Enhancement Using Arithmetic
  • Understanding Logic Operations
  • Basics of Spatial Filtering
  • Understanding Smoothing Spatial Filters
  • Sharpening Spatial Filters
  • How to Combine Spatial Enhancement Methods?

Image Enhancement in the Frequency Domain

  • Introduction to the Fourier Transform
  • Understanding the Frequency Domain
  • Explaining Smoothing Frequency-Domain Filters
  • Describing Sharpening Frequency Domain Filters
  • Homomorphic Filtering

Image Restoration

  • Understanding Model of the Image Degradation/Restoration Process
  • Various Noise Models
  • Restoration in the Presence of Noise Only-Spatial Filtering
  • Understanding Periodic Noise Reduction by Frequency Domain Filtering
  • Explaining Linear and Position Invariant Degradations
  • Estimating the Degradation Function
  • Understanding Inverse Filtering
  • Explaining Minimum Mean Square Error (Wiener) Filtering
  • Understanding Constrained Least Squares Filtering
  • Describing Geometric Mean Filter
  • Defining Geometric Transformations

Color Image Processing

  • Understanding Color Fundamentals
  • Explaining Color Models
  • Describing Pseudo-color Image Processing
  • Full-Color Image Processing
  • Defining Color Transformations
  • Understanding Smoothing and Sharpening
  • Explaining Color Segmentation
  • Describing Noise in Color Images
  • Explaining Color Image Compression

Wavelets and Multi-resolution Processing

  • Describing Multi-resolution Expansions
  • Explaining Wavelet Transforms in One Dimension
  • Understanding The Fast Wavelet Transform
  • Describing Wavelet Transforms in Two Dimensions
  • Understanding Wavelet Packets

Image Compression

  • Basics of image compression
  • Various Image Compression Models

Elements of Information Theory

  • Understanding Error-Free Compression
  • Explaining Lossy Compression
  • Describing Compression Standards

Morphological Image Processing

  • Preliminaries
  • Understanding Dilation and Erosion
  • Describing Opening and Closing
  • Explaining the Hit-or-Miss Transformation
  • Basic Morphological Algorithms
  • Understanding Extensions to Gray-Scale Images

Image Segmentation

  • Detection of Discontinuities
  • Understanding Edge Linking and Boundary Detection
  • Explaining Thresholding
  • Region-Based Segmentation by Morphological Watersheds
  • Explaining The Use of Motion in Segmentation

Representation and Description

  • Understanding Boundary Descriptors
  • Explaining Regional Descriptors
  • Use of Principal Components for Description
  • Describing Relational Descriptors

Object Recognition

  • Explaining Patterns and Pattern Classes
  • Understanding Recognition Based on Decision
  • Describing Structural Methods

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