An image histogram is a graphic representation of the frequency counts of all allowable pixel intensities. Figure 3 shows a sample of an image with a skewed histogram.

Histogram equalization is a point process that redistributes the image's intensity distributions in order to obtain a uniform histogram for the image. Histogram equalization can be done in three steps:

1. Compute the histogram of the image.
2. Calculate the normalized sum of histogram.
3. Transform the input image to an output image.

Note that the resulting histogram is not truly uniform, but it is better distributed than before. Truly uniformed histograms for discrete images are difficult to obtained because of quantization.

A histogram-equalized sample with histogram and normalized sum is illustrated in the following Figure:

In this case:

1. The new intensity value of the pixel (4) which is H1 is 5.
2. The new intensity value of the pixel H2 is .
3. The new intensity value of the pixel H3 is .
4. The new intensity value of the pixel H4 is .
5. The new intensity value of the pixel H5 is .
6. The new intensity value of the pixel H6 is .
7. The new intensity value of the pixel H7 is .
8. The new intensity value of the pixel H8 is .
9. The new intensity value of the pixel H9 is .
10. The new intensity value of the pixel H10 is .
11. The new intensity value of the pixel H11 is .
12. The new intensity value of the pixel H12 is .
13. The new intensity value of the pixel H13 is .
14. The new intensity value of the pixel H14 is .
15. The new intensity value of the pixel H15 is .
16. The new intensity value of the pixel H16 is

can someone help me to answer H2 to H16, please?