Unsharp Masking (a) Use the programs developed above (and in Project 2) to implement high-boost filtering, as given in Eqs. (3.6-8, 3.6-9). The averaging part of the process should be done using the mask in Fig. 3.32(a). (b) Download Fig0340(a)(dipxe_text) and enhance it using the program you developed in (a). Your objective is to choose constant k so that your result visually approximates Fig. 3.40(e).

I need to help writing a Matlab Code without using the matlab preset codes from the image toolbox.

Letting f (x, y) denote the blurred image, unsharp masking is expressed in equation form as follows. First we obtain the mask: gmask(x, y) = f(x,y)-f(x, y) (3.6-8) Then we add a weighted portion of the mask back to the original image: g(x, y) = f(x, y) + k * gmax(x, y) (3.6-9) where we included a weight, k (k 0), for generality. When k = 1, we have unsharp masking, as defined above. When k> 1, the process is referred to as Letting f (x, y) denote the blurred image, unsharp masking is expressed in equation form as follows. First we obtain the mask: gmask(x, y) = f(x,y)-f(x, y) (3.6-8) Then we add a weighted portion of the mask back to the original image: g(x, y) = f(x, y) + k * gmax(x, y) (3.6-9) where we included a weight, k (k 0), for generality. When k = 1, we have unsharp masking, as defined above. When k> 1, the process is referred to as