22-03-2012, 02:33 PM
Image is NOT Perfect Sometimes
image_enhancement.ppt (Size: 3.13 MB / Downloads: 103)
Image Enhancement
Introduction
Spatial domain techniques
Point operations
Histogram equalization and matching
Applications of histogram-based enhancement
Frequency domain techniques
Unsharp masking
Homomorphic filtering*
Recall:
There is no boundary of imagination in the virtual world
In addition to geometric transformation (warping) techniques, we can also photometrically transform images
Ad-hoc tools: point operations
Systematic tools: histogram-based methods
Applications: repair under-exposed or over-exposed photos, increase the contrast of iris images to facilitate recognition, enhance microarray images to facilitate segmentation.
Summary of Point Operation
So far, we have discussed various forms of mapping function f(x) that leads to different enhancement results
MATLAB function >imadjust
The natural question is: How to select an appropriate f(x) for an arbitrary image?
One systematic solution is based on the histogram information of an image
Histogram equalization and specification
How to Adjust the Image?
Histogram equalization
Basic idea: find a map f(x) such that the histogram of the modified (equalized) image is flat (uniform).
Key motivation: cumulative probability function (cdf) of a random variable approximates a uniform distribution