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03-09-2017, 11:44 PM

plz provide report on image enhancement. Using histogram equalization

jaseela123d · 05-09-2017, 04:18 PM

Histogram equalization is a method in processing contrast adjustment images using the histogram of the image.

This method consists of the overall contrast of many images, especially when the usable data of the image are represented by near contrast values. Through this adjustment, the intensities can be better distributed in the histogram. This allows areas of lower contrast. The equalization of the histogram is achieved by effectively extending the most frequent intensity values.

The method is useful in bright and dark background images and close-ups. In particular, the method can lead to better views of the structure in x-ray images and better detail in photographs that are over underexposed. A key advantage of the method is that it is a fairly simple person and an invertible operator. Thus, in theory, if the equation function of the histogram is known, the original of the histogram can be retrieved. The calculation is not computationally intensive. A disadvantage of the method is that it is indiscriminate. It can increase the contrast of the background noise, while decreasing the usable signal.

In scientific images where spatial correlation is more important than signal intensity (such as separation of DNA fragments from quantified length), the small signal-to-noise ratio is often difficult to detect.

The equalization of the histogram often produces unrealistic effects in the photographs; however, it is very useful for scientific images such as thermal, satellite or X-ray images, often the same class of images to which the false color is applied. Histogram equalization can also produce undesirable effects (such as a visible image gradient) when applied to images with low color depth. For example, if applied to the 8-bit image that is displayed with an 8-bit grayscale palette, it will further reduce the color depth (number of unique gray tones) in the image. Histogram equalization will work best when applied to images with a color depth much greater than the size of the palette, such as continuous data or 16-bit grayscale images.

There are two ways to think and implement equalization of the histogram, either as a change of image as a change of palette. The operation can express as P (M (I)) where the original image is, is the histogram equalization mapping operation and P is a palette. If we define a new palette as P '= P (M) and leave the image I unchanged, the histogram equalization is implemented as a palette change. On the other hand, if the palette P remains unchanged and the image is modified as I '= M (I), and the implementation is by image change. In most cases, the palette change is better, and it retains the original data.

Modifications of this method use multiple histograms, called subhistograms, to emphasize local contrast, in the place of general contrast. Examples of story methods incorporate adaptive histogram equalization, CLAHE contrast limitation histogram equalization, multiple histogram equation (MPHE), and optimized beta equation optimized beta biographer (MBOBHE). The purpose of these methods, especially MBOBHE, is to improve the contrast and the effect of the medium displacement luminosity and the loss of detail by modifying the scale.

A transformation of the sign equivalent to the equation of the histogram also appears to occur in biological neural networks to maximize the output firing rate of the neuron as a function of input statistics. This has been demonstrated particularly in the retina of the fly.

Histogram equalization is a specific case of the more general class of histogram reassignment methods. These methods seek to adjust the image to facilitate analysis or improve visual quality (eg retinex)

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