1. Digital Image Processing Image Enhancement in Spatial Domain
Dr. Abdul Basit Siddiqui

2. Image Sampling and Quantization
To create a digital image, we need to convert the continuous sensed data into digital form --> this involves sampling and quantization
Digitizing the coordinate values is called sampling
Digitizing the amplitude values is called quantization
---> see Fig in the textbook
In practice, the method of sampling is determined by the sensor
arrangement used to generate the image
The quality of a digital image is determined by the number of samples
and the number of gray levels
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3. Spatial resolution and intensity resolution
Sampling is the principal factor defining the spatial resolution of an image, and quantization is the principal factor defining the intensity resolution
Spatial resolution - number of rows and columns for example: 128 x128, 256 by 256, etc.; -->see Figs and 2.20
Intensity resolution - number of gray levels for example: 8 bits, 16 bits, etc.; --->see Fig. 2.21
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4. Image Enhancement
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5. Image Enhancement
Process an image to make the result more suitable than the original image for a specific application
–Image enhancement is subjective (problem /application oriented)
Image enhancement methods:
Spatial domain: Direct manipulation of pixel in an image (on the image plane)
Frequency domain: Processing the image based on modifying the Fourier transform of an image
Many techniques are based on various combinations of methods from these two categories
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6. Image Enhancement
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7. Basic Concepts
Spatial domain enhancement methods can be generalized as
g(x,y)=T[f(x,y)]
f(x,y): input image
g(x,y): processed (output) image
T[*]: an operator on f (or a set of input images), defined over neighborhood of (x,y)
Neighborhood about (x,y): a square or rectangular sub-image area centered at (x,y)
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8. Point Processes
Point processes are the simplest of basic image processing operations.
A point operation takes a single input image into a single output image in such a way that each output pixel's gray level depends only upon the gray level of the corresponding input pixel.
Thus, a point operation cannot modify the spatial relationships within an image.
Point operations transform the gray scale of an image.
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9. Point Processes Linear and nonlinear point operations Examples:
Contrast Stretching
Image Negatives
Intensity-level Slicing
Bit-plane Slicing
Other Intensity Transformations
Histogram Equalization
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10. Basic Concepts
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11. Basic Concepts g(x,y) = T [f(x,y)] Pixel/point operation:
Neighborhood of size 1x1: g depends only on f at (x,y)
T: a gray-level/intensity transformation/mapping function
Let r = f(x,y) s = g(x,y)
r and s represent gray levels of f and g at (x,y)
Then s = T(r)
Local operations:
g depends on the predefined number of neighbors of f at (x,y)
Implemented by using mask processing or filtering
Masks (filters, windows, kernels, templates) :
a small (e.g. 3×3) 2-D array, in which the values of the
coefficients determine the nature of the process
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12. Common Pixel Operations
Image Negatives
Log Transformations
Power-Law Transformations
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13. Image Negatives Reverses the gray level order
For L gray levels the transformation function is
s =T(r) = (L - 1) - r
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14. Image Negatives
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15. s =T(r) = a.r (a is a constant)
Image Scaling
s =T(r) = a.r (a is a constant)
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16. Function of s = cLog(1+r)
Log Transformations
Function of s = cLog(1+r)
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17. Log Transformations Properties of log transformations Application:
–For lower amplitudes of input image the range of gray levels is expanded
–For higher amplitudes of input image the range of gray levels is compressed
Application:
This transformation is suitable for the case when the dynamic range of a processed image far exceeds the capability of the display device (e.g. display of the Fourier spectrum of an image)
Also called “dynamic-range compression / expansion”
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18. Log Transformations
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19. Power-Law Transformation
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20. Power-Law Transformation
For γ < 1: Expands values of dark pixels, compress values of brighter pixels
For γ > 1: Compresses values of dark pixels, expand values of brighter pixels
If γ=1 & c=1: Identity transformation (s = r)
A variety of devices (image capture, printing, display) respond according to power law and need to be corrected
Gamma (γ) correction
The process used to correct the power-law response phenomena
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21. Power-Law Transformation
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22. Gamma Correction
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23. Histogram
Gray level histogram of an image - a function showing for each gray level the number of
pixels in the image that have that gray level; it is simply a bar graph of the pixel intensities
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24. Histogram
Histogram gives us a convenient, easy-to-read representation of concentration of pixels versus intensity in an image
Dynamic range - an range of intensity values that occur in an image
Contrast stretching - if image has low-dynamic range; low-dynamic range can result from poor illumination, lack of dynamic range in imaging sensor, wrong setting of the sensor parameters, etc.
Compression of dynamic range - if the dynamic range of the image far exceeds the capability of the display device
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25. Histogram calculation
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26. Examples of several types of image histograms:
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27. Histogram Equalization
Images with poor intensity distributions can often be enhanced with histogram equalization <-- point process
The goal is to obtain a uniform histogram
• Histogram equalization will not flatten a histogram; if a histogram has peaks and valleys it will still have them after equalization - they will be shifted and spread over the entire range of image intensities
Works best on images with fine details in darker regions
Use it carefully - good images can be often degraded by histogram equalization
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30. Histogram of the equalized image
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31. Histogram Equalization-Example
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