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Software for Teaching and Learning
Contrast Enhancement of an Image
This demo is an introduction to a relatively straightforward type of image processing known as histogram equalization that may be used to enhance image contrast. This technique involves point processing: an algorithm computes output pixels by inputting individual pixels. This is different than neighborhood processing (see other demos) in which groups of pixels are used as input. Enhancement of astronomical images is an area that helped give birth to the field Image Processing. This type of enhancement permitted astronomers to better interpret images of distant bodies. Because astronomical objects are dim and because of the rotation of the Earth, there are some fundamental limits on image quality that is obtainable. Lengthy exposures improve image contrast, but introduce blurring due to the Earth’s rotation. Brief exposures eliminate motion blurring but yield relatively dim images that may hide details. This is the case for an image of the Hale-Bopp comet, shown below.
Image of the Hale-Bopp comet. Some details of the comet are not obvious in this image, but are revealed by contrast enhancement.
A second tail of the comet is revealed by the contrast enhancement. (The fuzzy regaions result from the enhancement of errors introduced by compression). The method of histogram equalization is derived from basic probability theory. A PDF of the gray levels in the input image is first estimated via a histogram. Then the gray levels are remapped to new values in a manner that 'spreads out' the area under the PDF curve to make the histogram somewhat flatter. The histogram is 'spread out' in order to span the entire dynamic range of an image (0-255 here). The demonstration includes a display of the input and output images as well as the input histogram and gray level mapping function. |
Learning ObjectivesThe following learning objectives are associated with the above demonstration, with supporting lecture and possible programming assignment.
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