You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: guidedfilter.m
    path of file: ./repos/underwater_dark_chennel
    
    the code of the file until where you have to start completion: function q = guidedfilter(I, p, r, eps)
%   GUIDEDFILTER   O(1) time implementation of guided filter.
%
%   - guidance image: I (should be a gray-scale/single channel image)
%   - filtering input image: p (should be a gray-scale/single channel image)
%   - local window radius: r
%   - regularization parameter: eps

[hei, wid] = size(I);
N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.


    
function q = guidedfilter(I, p, r, eps)
%   GUIDEDFILTER   O(1) time implementation of guided filter.
%
%   - guidance image: I (should be a gray-scale/single channel image)
%   - filtering input image: p (should be a gray-scale/single channel image)
%   - local window radius: r
%   - regularization parameter: eps

[hei, wid] = size(I);
N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.

% imwrite(uint8(N), 'N.jpg');
% figure,imshow(N,[]),title('N');


mean_I = boxfilter(I, r) ./ N;
mean_p = boxfilter(p, r) ./ N;
mean_Ip = boxfilter(I.*p, r) ./ N;
cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.

mean_II = boxfilter(I.*I, r) ./ N;
var_I = mean_II - mean_I .* mean_I;

a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;
b = mean_p - a .* mean_I; % Eqn. (6) in the paper;

mean_a = boxfilter(a, r) ./ N;
mean_b = boxfilter(b, r) ./ N;

q = mean_a .* I + mean_b; % Eqn. (8) in the paper;
end