Image Processing in MATLAB

Part - I : Basic Image Processing

1. Introduction to Image Processing
 
2. Introduction to Images in MATLAB:
 
  •    Overview of Image Types in MATLAB: Binary, Indexed, Grayscale, Truecolor Images
  •    Image Coordinate Systems: Pixel & Spatial Coordinates
  •    Converting Between Image Types
  •    Converting Between Image Classes
  •    Simple Image Arithmetic
 
3. Introductory Examples:
 
Example 1 - Reading and Writing Images
 
  •   Read and Display an Image
  •   Check How the Image Appears in the Workspace
  •   Improve Image Contrast
  •   Write the Image to a Disk File
  •   Check the Contents of the Newly Written File
Example 2 - Analyzing Images
  •    Read Image
  •    Use Morphological Opening to Estimate the Background
  •    View the Background Approximation as a Surface
  •    Subtract the Background Image from the Original Image
  •    Increase the Image Contrast
  •    Threshold the Image
  •    Identify Objects in the Image
  •    Examine One Object
  •    View All Objects
  •    Compute Area of Each Object
  •    Compute Area-based Statistics
  •    Create Histogram of the Area
 
4. Displaying and Exploring Images using the "imtool" GUI
  •     Displaying Images Using the imshow Function
  •     Using the Image Tool to Explore Images
  •     Using Image Tool Navigation Aids
  •     Getting Information about the Pixels in an Image
  •     Measuring the Distance Between Two Pixels
  •     Getting Information About an Image Using the Image Information Tool
  •     Adjusting Image Contrast Using the Adjust Contrast Tool
  •     Cropping an Image Using the Crop Image Tool
  •     Viewing Image Sequences
  •     Displaying Different Image Types
  •     Adding a Colorbar to a Displayed Image
  •     Printing Images
 
Part - II: Mathematical Aspects of Image Processing
 
5. Sampling & Quantization
  •   Introduction to terms
  •   Isopreference Curves
  •   Non-Uniform Sampling
  •   Point-Spread Function (PSF)
  •   Physical Resolution

6. Image Enhancement in the Spatial Domain
  •   Spatial Domain Methods
  •   Convolution & Correlation
  •   Point Processing
  •   Neighborhood Processing
                 - Low-Pass Filtering (Smoothing)
                 - Salt and Pepper Noise Filtering
                 - Low-Pass Averaging Filter
                 - Low-Pass Median Filtering
 
  •   High-Pass Filtering
  •   High-Boost Filtering
  •   Performing Linear Filtering of Images Using imfilter
  •   Filtering an Image with Predefined Filter Types
  •   Zooming
                 - Replication
                 - Linear Interpolation
 
7. Further Applications of Spatial Transformations
  •    Resizing an Image
  •    Rotating an Image
  •    Cropping an Image
  •    Performing General 2-D Spatial Transformations
  •    Self Exercise:

                   a. Image Transformations
                   b. Finding the Rotation and Scale of a Distorted Image

8. Image Registration
  •   Registering an Image
  •   Transformation Types
  •   Selecting Control Points
  •   Using Correlation to Improve Control Points
  •   Self Exercises:
                   a. Registering an Aerial Photo to an Orthophoto
                   b. Registering an Image Using Normalized Cross-Correlation
 
9. Image Enhancement Based on Histogram Modelling
  •   Linear Stretching
  •   Histogram Equalisation
  •   Histogram Specification

10. Image Enhancement in the Frequency Domain
  •   The Fourier Transform
  •   1-D Fourier Transform
  •    2-D Fourier Transform
  •    Discrete Fourier Transform (DFT)
  •    Low-Pass Frequency Domain Filters
                -  Ideal Low-Pass Filter
-               -  Butterworth Low-Pass Filter
                -  Gaussian Low-Pass Filter
 
  •    High-Pass Frequency Domain Filters
                 -  Ideal High-Pass Filters
                 -  Butterworth High-Pass Filter
                 - Gaussian High-Pass Filter
 
  •    High-Boost Filtering
  •    Homomorphic Filtering
  •    Periodic Noise (Sinusoidal Noise)
  •    Relationship Between Filtering in Spatial and Frequency Domains
 
11. Image Segmentation
  •   Point Detection
  •   Line Detection
  •   Edge Detection
  •   ROI-Based Processing
                 - Specifying a Region of Interest (ROI)
                 - Filtering an ROI
                 - Filling an ROI
  •  Self Exercises:

           a) Detecting a Cell Using Image Segmentation (Image Detection using templated Image Segmentation)
            b) Identifying Round Objects (Image Shape Recognition)

 

PART - III Applied Image Processing Using only MATLAB Functions (Optional)
 
1. Analyzing and Enhancing Images
  •   Getting Information about Image Pixel Values and Image Statistics
  •   Analyzing Images
  •   Analyzing the Texture of an Image
  •   Adjusting Pixel Intensity Values
  •   Removing Noise from Images

2. Getting Information about Image Pixel Values and Image Statistics
  •   Getting Image Pixel Values Using impixel
  •   Creating an Intensity Profile of an Image Using improfile
  •   Displaying a Contour Plot of Image Data
  •   Creating an Image Histogram Using imhist
  •   Getting Summary Statistics About an Image
  •   Computing Properties for Image Regions

3. Analyzing Images
  •   Detecting Edges Using the edge Function
  •   Tracing Object Boundaries in an Image
  •   Detecting Lines Using the Hough Transform
  •   Analyzing Image Homogeneity Using Quadtree Decomposition

4. Analyzing the Texture of an Image
  •   Understanding Texture Analysis
  •   Using Texture Filter Functions
  •   Using a Gray-Level Co-Occurrence Matrix (GLCM)

5. Adjusting Pixel Intensity Values
  •   Understanding Intensity Adjustment
  •   Adjusting Intensity Values to a Specified Range
  •   Adjusting Intensity Values Using Histogram Equalization
  •   Adjusting Intensity Values Using Contrast-Limited Adaptive Histogram Equalization
  •   Enhancing Color Separation Using Decorrelation Stretching

6. Removing Noise from Images
  •    Understanding Sources of Noise in Digital Images
  •    Removing Noise By Linear Filtering
  •    Removing Noise By Median Filtering
  •    Removing Noise By Adaptive Filtering

7. Image Deblurring
  •   Understanding Deblurring
  •   Deblurring with the Wiener Filter
  •   Deblurring with a Regularized Filter
  •   Deblurring with the Lucy-Richardson Algorithm
  •   Deblurring with the Blind Deconvolution Algorithm
  •   Avoiding Ringing in Deblurred Images
  •   Self Exercises:
                    a. Deblurring Images Using Blind Deconvolution Algorithm, Lucy-Richardson Algorithm, Regularized Filter and Wiener Filters
                    b. Contrast Enhancement Techniques
                    c. Correcting Nonuniform Illumination & Performing Image Analysis.
 

PART - IV: Advanced Image Processing Techniques (Excluded in this course)
 
       1. Morphology Representation and Description
       2. Discrete Image Transforms
       3. Image Compression
       4. Wavelet Transforms
       5. Colour Image Processing