In the era of image processing, scientific analysis, digital photography, remote sensing and in visualization, medical image analysis, surveillance system; image enhancement plays a vital role. By enhancement of image noise can be reduced and it can remove artifacts. A special feature of image enhancement is that it can hold all the details of image after enhancement.
As we know contrast enhancement of an image is done by making light colors lighter and dark colors darker at the same time. And this process is done by setting all color components below a specified lower bound to zero, and all color components above a specified upper bound to the maximum intensity. Here various methods of histogram equalization is addressed. Histogram graphically shows the distribution of pixels among grey scale values. Dynamic range of an image can be improved by equalization method. Histogram equalization is an efficient and useful technique.
The intensities will be equally distributed in output image after the process of histogram equalization.
There are some reasons that led to the need of enhancement:
- bad quality of the used imaging device,
- lack of expertise of the operator
- The adverse external conditions or environment condition at the time of capture.
Processing of images to extract some specific features of an image is called image enhancement. The main motive of image enhancement is to improve the original image for some specific applications. It sharpens or improves image features such as boundaries, or contrast to make a better graphic display, and better analysis
Image enhancement has two categories:
1) Spatial domain method
2) Frequency domain method
Spatial domain method is based on direct manipulation of pixels in an image. Frequency domain method is based on modifying FT of an image.
Enhancement of an image is done by sharpening, noise removal and brightness increment. Unfortunately there is no general theory for determining what ‘good’ image enhancement, when it comes to human perception. If it looks good .it is good!
Operation of image enhancement is shown by given block diagram
Histogram equalization is distribution of particular type of data. It plays a vital role in image processing. By histogram equalization we can improve contrast and appearance of an image. Entire spectrum of pixels (0-255) will be stretches by histogram equalization. A histogram that covers all possible values which is used by gray scale is determined as a good histogram. A good histogram tends to have good contrast and the details of an image that may be easily observed.
In particular, the method can lead to better views of bone structure in x-ray images, and to better detail in photographs that are over or under-exposed.
Here some advantage and disadvantage of this method
Advantage: A key advantage of the method is that it is a fairly straight forward technique and an invertible operator. So in theory, if the histogram equalization function is known, then the original histogram can be recovered.
Disadvantage: A disadvantage of the method is that it is indiscriminate. It may increase the contrast of background noise, while decreasing the usable signal.
Histogram equalization is a specific case of the more general class of histogram remapping Methods. These methods seek to adjust the image to make it easier to analyze or improve the visual quality.
As the methods of histogram equalization are histogram expansion, cumulative distributive equalization, par sectioning, odd sectioning, and local area histogram equalization.
Process of histogram equalization using cumulative distribution function have described in given figure.
Process of histogram equalization