The document discusses several image processing examples involving point operations and histogram matching/equalization. It includes 5 problems: 1) matching the histogram of an image to a target histogram, 2) approximately equalizing the histogram of an image, 3) matching an image histogram to a target, 4) equalizing the histogram of an 8x8 image with values from 0-7, and 5) approximately equalizing the histogram of another image to have values from 0-7. The document provides instructions for finding transformation functions to perform the specified histogram operations and outputting resulting histograms and processed images.

1. Lecture 5: Point Operations and Histogram Examples
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1-For the image shown in Fig. A, find a transformation function (i.e. a look-up-table) that
will change its histogram to match the one shown in Table A. Draw the transformed
image in Fig. B. Assume that the processed images can only take integer values
between 0 and 3 (including 0 and 3), and give the histograms of the original and
processed images in Table B and Table C, respectively

2. Lecture 5: Point Operations and Histogram Examples
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2- For the image shown on the left below, find a transformation function that will approximately
equalize its histogram, and draw the transformed image in the grid on the right. Give the
histogram of the original image, the histogram of the processed image, and the transformation
function (in a lookup table).Assume that the processed images can only take integer values
between 0 and 7 (including 0 and 7).
Histogram of original image:

3. Lecture 5: Point Operations and Histogram Examples
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3-For the image shown in Fig. A, find a transformation function (i.e. a look-up-table) that
will change its histogram to match the one shown in Table A. Draw the transformed
image in Fig. B. Assume that the processed images can only take integer values
between 0 and 3 (including 0 and 3), and give the histograms of the original and
processed images in Table B and Table C, respectively.

4. Lecture 5: Point Operations and Histogram Examples
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4- Equalize the histogram of the 8 × 8 image below. The image has grey levels 0, 1, . . . , 7.
Histogram equalization spreads the histogram of an image so that it will span a wider range
of gray scale values. This usually results in enhancing the contrast, which is often useful to
improve the visibility of details in images. The discrete equalization is given by

5. Lecture 5: Point Operations and Histogram Examples
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NOTE: This solution is the “official” one for the course, however, it has some problems. For
instance if one tries to equalize an image where the histogram is already fully equalized (i.e. 8
pixels for each level) the resulting histogram will not remain the same. Alternative solutions
and discussion were presented in exercise session 4.
5-For the image shown in Fig. 2(a), find a transformation function that will
approximately equalize its
histogram, and draw the transformed image in Fig. 2(b), and give the histograms of the
processed image.
Assume that the processed images can only take integer values between 0 and 7
(including 0 and 7),

6. Lecture 5: Point Operations and Histogram Examples
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