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5700_1 (Why Processing Digital Images (Acquire an image, Prepare for…

- - - - Sampling = Original continuous signal × the Dirac comb function
      - Nyquist rate
  - - - How to assign values to the new matrix
        
        Original value at every-other row/column (1; 3; : : :)
        
        Nearest neighbor interpolation: Duplicate value at the rest
        grids (2; 4; : : :)
        
        Bilinear interpolation
        
        Spline interpolation
      - Smoothing often used to make image looking better
      - Quantitative measure is a better criterion in judging the
        zoomed image quality
- - - - Periodic Noise Reduction using Frequency Domain
        Filtering Methods
        
        Band-reject filter
        
        Band-pass filter
        
        Notch filter
      - Restoration Using Spatial Filtering
        
        Mean filter
        
        Order-statistics filter: median, max, min, midpoint
        
        Adaptive filter
- - - - Method 1: Clipping and Thresholding
      - Method 2: Image negatives.
      - Method 3: Log Transformations
      - Method 4: Power-Law Transforms
      - Piecewise-Linear Transformation:
        
        Contrast stretching:
        
        Gray-level slicing:
        
        Bit-plane slicing:
    - - Histogram
        
        Histogram equalization
        
        Histogram specication
    - - Spatial Filtering consists of：A neighborhood A predened operation
        
        Linear spatial filtering
        
        Correlation: Process of moving the lter mask over the image
        and computing the sum of products at each location
        
        Convolution: Same as correlation except rotating
        the mask by 180
        
        Smooth Linear Spatial Filtering
        
        Average mask
        
        Smooth Order-statistic (Nonlinear) Filtering
        
        median filter, max filter, min filter
        
        Sharpening filter
        
        Laplacian
        
        Gradient:
        
        Unsharp masking
        
        Highboost
      - Filtering in Frequency Domain
        
        Transformations
        
        What is Transform?
        
        Forward transform
        
        Inverse transform
        
        Fourier Transform
        
        Merits
        
        Easy to understand the concept.
        Easy to design the filter shape.
        
        Drawbacks
        
        higher computational load than spatial methods.
        Complication due to digitization in both domains.
        
        Fourier transform and inverse Fourier transform
        
        Classication of Filters
        
        Low-pass filter
        Serve for smoothing/blurring effect
        
        Ideal low-pass filter : sharp
        
        Blurring effect
        
        Ringing effect
        
        Gaussian filter : smooth
        
        No ringing effect
        
        Butterworth filter : between
        
        Ringing effect vs. order n
        
        High-pass filter
        Shape flipped in the frequency and spatial domain
        
        Ideal High Pass Filter
        
        Gaussian High Pass Filter
        
        Butterworth High Pass
        
        Laplacian Filter
        
        Unsharp Masking Filtering
        
        High-Boost Filtering
        
        High-Frequency Emphasis Filtering
        
        Homomorphic Filtering
        
        Band-pass filter.