[Gonzalez & Woods] Digital Image Processing: 3. Image Enhancement in the Spatial Domain

Digital Image Processing (2nd ed.)
3. Image Enhancement in the Spatial Domain



> Image enhancement
1) spatial domain methods
2) frequency domain methods - Fourier transform

Visual evaluation of image quality is a hightly subjective process.

3.1 Background

> point processing
gray-level (/ intensity / mapping) transformation function
contrast stretching
thresholding function

> mask processing / filtering
mask / filter / kernel / template / window
image sharpening

3.2 Some Basic Gray Level Transformations

one-dimensional array
table lookup

3.2.1 Image Negatives
- to enhance white or gray detail embedded in dark regions of an image

3.2.2 Log Transformations
- to expand the values of dark pixels in am image while compressing the higher-level values
- to compress the dynamic range of images with large variations in pixel values

eg. Fourier spectrum
cf. net effect -> a significant degree of detail will be lost in the display of a typical Fourier spectrum

 
3.2.3 Power-Law Transformations
- gamma correction (for CRT) - with the device-dependent value of gamma
- contrast enghancement - expansion / compression of gray levels

3.2.4 Piecewise-Linear Transformation Functions

Contrast stretching
- to increase the dynamic range of the gray levels in the image being processed

Gray-level slicing
- to highlight a specific range of gray levels in an image

Bit-plane slicing
- to analyze the relative importance played by each bit of the image -> image compression

3.3 Histogram Processing

histogram => image statistics -> real-time image processing

eg.
narrow histogram -> low contrast
uniform distribution -> high contrast -> detail, more -> high dynamic range

3.3.1 Histogram Equalization

http://en.wikipedia.org/wiki/Monotonic_function

cf. http://en.wikipedia.org/wiki/Multi-valued_function

http://en.wikipedia.org/wiki/Probability_density_function
http://mathworld.wolfram.com/ProbabilityDensityFunction.html

http://en.wikipedia.org/wiki/Cumulative_distribution_function (누적 분포 함수)

The pdf of the transformed histogram is always uniform and independent of the pdf of the original histogram.

histogram = plot of pdf of gray levels versus gray levels

histogram equalization (/ histogram linearization)
= mapping each pixel in the input image into a corresponding pixel in the output image
=> to spread the histogram of the input image
-> the levels of the histogram-equalized image will span a fuller range of the gray scale


3.3.2 Histogram Matching (Specification)