3.5.4 Problem In Histogram Equalization

3.5.4 Problem in Histogram Equalization
1. This histogram equalization method does not take the mean brightness of an image into account.
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2. The HE method may result in over enhancement and saturation art effects due to the stretching of the gray levels over the full gray level range.
3. HE can be found on the fact that the brightness of an image can be changed after the HE.
4. Nevertheless, HE is not commonly used in consumer electronics such as TV because it may significantly change the brightness of an input image and cause undesirable art effects.
5. It can be observed that the mean brightness of the histogram-equalized image is always the middle gray level regardless of an input mean.
3.5.5 Adaptive Histogram Equalization
Adaptive histogram
This feature can also be applied to global histogram equalization, giving rise to contrast limited histogram equalization (CLHE), which is rarely used in practice.CLAHE was originally developed for medical imaging and has been successful for the enhancement of portal images [23]. In the case of CLAHE, the contrast limiting procedure has to be applied for each neighborhood from which a transformation function is derived. CLAHE was developed to prevent the over amplification of noise that adaptive histogram equalization can give rise to. This is achieved by limiting the contrast enhancement of AHE. The contrast amplification in the vicinity of a given pixel value is given by the slope of the transformation function. This is proportional to the slope of the neighborhood cumulative distribution function (CDF) and therefore to the value of the histogram at that pixel value. CLAHE limits the amplification by clipping the histogram at a predefined value before computing the CDF. This limits the slope of the CDF and therefore of the transformation function. The value at which the histogram is clipped, the so-called clip limit, depends on the normalization of the histogram and thereby on the size of the neighborhood region. Common values limit the resulting amplification to between 3 and 4 times the histogram mean
At first, the image to be segmented is taken as input in JPG format. The image is read by MATLAB with the help of ‘imread’ command and returns the image data in the array RGB (M×N×3). Next, the image is converted from RGB to grayscale image with the help of „rgb2gray‟ command. The fusion of various gray scale images is maintained by local contrast enhancement method. There
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are three techniques of image enhancement used in this thesis. These techniques are used for performing of fusion method. After that grayscale, contrast limited adaptive histogram equalization method is obtained with the help of the function ‘adapthiste as shown in figure below.
Figure 3.24 flow chart
3.8 Main Flow chart
This technique can be limited in order to avoid noise. Next step is to call the histogram equalization to obtain with the help of function ‘histeq’. It is used for the value of intensity over brightness in order to achieve high contrast. Histogram equalization image information are then
RGB Color Image
Gray Image
Proposed Image
Contrast Limited Adaptive Histogram Equalization Image
Histogram Equalization Image
Imadjust