23-09-2016, 12:12 PM
1455853869-project.pptx (Size: 144.69 KB / Downloads: 3)
Histogram equalization:
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram.
Histogram equalization automatically determine the transformation function. That seek to produce an output image that has a uniform histogram.
Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modeling[1]
Gaussian mixture model is used.
Automatically parameter setting for a given dynamic range.
Dense data with low standard deviation should be dispersed, whereas scattered data with high standard deviation should be compacted.
Merits:
Brightness preservation.
DE preservation.
Contrast improvement.
Demerits:
Weak edge information.
Feature histogram equalization for feature contrast enhancement [2]
The FHE is a well-known image histogram equalization.
The FHE aims to increase the discrimination by modifying their vector-component values through an equalization process.
The IHE aims to increase the visibility of image contents by altering the pixel intensity values.
Merits:
More accurate point-to-point correspondence links have been established.
Demerits:
Can be used only in low feature contrast image.
An advanced gradient histogram and its application for contrast and gradient enhancement[4]
The AGH contains both gradient and intensity information of image.
Excessive contrast enhancement are used.
AGHE can increase the mean of absolute gradients which is a measurement of image gradient.
Merits:
Improve the image quality effectively.
Yields higher gradient strength.
Demerits:
Original image become too bright
Histogram matching(specification):
Histogram equalization has a disadvantage which is that it can generate only one type of output image.
With Histogram Specification, we can specify the shape of the histogram that we wish the output image to have.
It doesn’t have to be a uniform histogram.
Histogram specification is a trial-and-error process.