Problem 2. Adaptive Histogram Equalization (10 points) It is often found in image processing and related fields that real world data is unsuitable for direct use. This warrants the inclusion of pre-processing steps before any other operations are performed. An example of this is histogram equalization (HE) and its extension adaptive histogranm equalization (AHE) The goal of this problem is to implement a MATLAB function for AHE as described in Chapter 1 of Adaptive Histogram Equalization - A Parallel Implementation1. The function has the following specifications (i) The desired function AHE) takes two inputs: the image im and the contextual region size win size (ii) Using the pseudocode in Algorithm 1 as a reference, compute the enhanced image after AHE. (iii) You may use loops if necessary. You should not make use of any inbuilt MATLAB functions for AHE or HE. (iv) The function returns one output: the enhanced image after AHE. Algorithm 1 Pseudocode for Adaptive Histogram Equalization of an Image *Pre-requirement : Pad the image im on all 4 sides by mirroring intensity values so that the contextual region for edge pixels remains valid. This can be done in MATLAB using padarray with the 'symmetric' argument. 1: function AHE(im, win size) 2: for each pixel (x, y) in image im do 3: rank 0 contextual-region' (win-size win-size) window centered around (x,y) for each (i,j) in contertual region do 6: if im(x,y) > im(i,j) then rank rank+ 1 output(x, y) im(i,j) then rank rank+ 1 output(x, y)