You have been selected as finalist as an image processing engineer for a local company. As part of the final interview, you have been asked to demonstrate your

programming proficiency by implementing some basic processing algorithms.

Part $1$: Convert RGB Image to Grayscale

Import a color image into Matlab as a $3-$dimensional Red, Green, and Blue $($RGB$)$ array of $8-$bit color planes.

Scale each color plane to values between $0$ and $1.$

Write a function to convert the color image to grayscale by scaling each color plane by coefficients $C_R,C_G,$ and, $C_B.$ The resulting grayscale image is given by

Gray Image $=C_R*R+C_G*G+C_B*B.$ The coefficients are governed by the constraint $C_R+C_G+C_B=1.$

The inputs to function are the name of the input array and the $3$ color plane coefficients $C_R,C_G,$ and $,C_B.$

Part $2$: $33$ Kernel Filtering

Generate the $33$ kernels required to implement the following filters

Mean Filter

Vertical Edge Filter

Horizontal Edge Filter

Write functions to apply the the three kernel filters to the grayscale image produced in Part $1$ of the project.

Use the Vertical and Horizontal Edge filters to produce an edge enhanced gray scale image.

Part $3$: Histogram Processing

Compute the $256$ bin histogram of the original grayscale and the mean filtered image.

Calculate the Kullback$-$Leibler divergence between the two images.

Use the grayscale image histogram to perform histogram equalization.