function EXsharpenImage(file,type);        % A. Salerno [BPHS 4090 F13]
% For specified image, a user-defined kernel (see below) is convolved with
% the image in the spatial domain. The two default kernels are a blurring
% and a sharpening [see also http://en.wikipedia.org/wiki/Kernel_(image_processing) ]
% ex. > EXsharpenImage('filename.png','s');
close all
% specify kernel to be used
if nargin<2
    type = 's';% Sharpening is default
end
if strcmp(type,'s') || strcmp(type,'sharp')
    ker = [-1 -1 -1
        -1 50 -1
        -1 -1 -1];
elseif strcmp(type,'b') || strcmp(type,'blur')
    ker = [1 1 1
        1 1 1
        1 1 1]; %Adaptation of blurring kernel on wiki
elseif strcmp(type,'e') || strcmp(type,'edge')
    ker = [-1 -1 -1
        -1 8 -1
        -1 -1 -1]; % edge detection
end

im = imread(file); % Imports an image
if numel(size(im)) == 3 %Only for rgb images
    im = rgb2gray(im); %converts the image into a grayscale
end

%Plot original Image in b/w
figure
subplot(1,2,1)
imagesc(im);    colormap(gray);  colorbar;
axis image; title('Original Image');
n = size(im);
imp = zeros(n(1)+2,n(2)+2); % imp is the matrix that will be used to process
% the data. It is the "extended matrix" used.
% The addition of 2 is for the border (i.e.
% there is an addition at the beginning and at
% the end)
imp(2:end-1,2:end-1) = im; % Set the centre of the processing matrix to
% be the original matrix - basis for how the
% image will be processed

%Set the corner values to be the same
imp(1,1) = im(1,1);
imp(end,1) = im(end,1);
imp(1,end) = im(1,end);
imp(end,end) = im(end,end);

% Set the centred "edge rows and columns" to be the same
imp(1,2:end-1) = im(1,:); % Top row
imp(end,2:end-1) = im(end,:); %Bottom row
imp(2:end-1,1) = im(:,1); %Left Column
imp(2:end-1,end) = im(:,end); %Right column

% Since the matrix can be scaled in multiple different ways, the top corner
% seems the easiest to do. Using the top corner of imp, I will scan across
% imp, multiplying the elements in 3x3 matricies, summing them, and then
% taking that value, and putting into 'fim' the Final IMage
im2 = zeros(n(1),n(2));
for i = 1:n(1) % Rows
    for j = 1:n(2) %columns
        var = ker.*imp(i:i+2,j:j+2);
        im2(i,j) = sum(sum(var));
    end
end
% In order to prevent errors, I will now normalize the matrix to it's
% greatest value, set this to be the max of the original matrix ensuring
% every value ends up as an integer
subplot(1,2,2)
imagesc(im2); colorbar;
axis image; title('Image with Kernel Applied')