Image enhancement (1) : The basics

Image enhancement involves improving the quality of the image, either for the purpose of aesthetic appeal, or for further processing. Therefore you are either enhancing features within an image, or suppressing artifacts. The basic forms of enhancement include:

  • contrast enhancement : enhancing the overall contrast of an image, to improve dynamic range of intensities.
  • noise suppression : reducing the effect of noise contained within an image
  • sharpening : improving the acuity of features within an image.

These relate to both grayscale and colour images, and there are additional mechanisms for colour images to deal with enhancing colour. The trick with any of these enhancement mechanisms is determining when they have achieved the required effect. In image processing this is often a case of the rest being “in the eye of the beholder”. A photo who’s colour has been enriched may seem pleasing to one person, and saturated to another.

To illustrate, consider the following example. This image is an 8-bit image that is 539×699 pixels in size.

ipeg

Original image

Here its associated histogram:

ipeghist

From both the image and histogram, it is possible to discern that the image lacks contrast, with the majority of gray intensities situated between 25 and 195. So one of the enhancements could be to improve its contrast. Here is the result of a simple histogram stretching:

ipegce

Contrast enhancement through histogram stretching

It may then be interesting to smooth noise in the image or, sharpen the image to enhance the letters in the advertising. The sub-image extracted from the above shows three different techniques (click on it to get the full effect).

ipegall

Forms of image enhancement (sub-image extracted from contrast enhanced image): original (top-left), noise suppression using a 3×3 mean filter (top-right), image sharpening using unsharp masking (bottom-left), and unsharp masking applied after mean filtering (bottom-right).

 

 

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