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
o Low-Pass Filtering (Smoothing)
o Salt and Pepper Noise Filtering
o Low-Pass Averaging Filter
o 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
o Replication
o 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


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