Question $1$c

The global histogram equalization technique is easily adaptable to local histogram equalization. The procedure is to define a square or rectangular window $($neighborhood$)$ and move the center of the window from pixel to pixel. At each location, the histogram of the points inside the window is computed and a histogram equalization transformation function is obtained. This function is finally used to map the intensity level of the pixel centered in the neighborhood to create a corresponding $($processed$)$ pixel in the output image. The center of the neighborhood region is then moved to an adjacent pixel location and the procedure is repeated. Since only one new row or column of the neighborhood changes during a pixel$-$to$-$pixel translation of the region, updating the histogram obtained in the previous location with the new data introduced at each motion step is possible. This

approach has obvious advantages over repeatedly computing the histogram over all pixels in the neighborhood region each time the region is moved one pixel location.

Write an M$-$function for performing local histogram equalization. Your function should have the following specifications.