Here’s how we do that in MATLAB: first, load up your blurry photo using the `imread` function. Then, convert it to grayscale (because color can be distracting when you’re trying to see details) and normalize its values so they range from 0-1 instead of -256 to 255. This is important because some deconvolution algorithms work better with normalized data.

Next, we need to create a “kernel” or “filter” that will help us restore the image’s sharpness. A common choice for this is called a “Gaussian filter,” which basically just blurs things out even more (which might seem counterintuitive, but trust me). We can generate one of these using MATLAB’s `fspecial` function:

% Load the blurry image and convert it to grayscale
img = imread('blurry_image.jpg'); % reads the image file and stores it in the variable "img"
img = rgb2gray(img); % converts the image to grayscale and overwrites the variable "img"

% Normalize its values so they range from 0-1 instead of -256 to 255
img = (img - min(img(:))) / max(img(:)); % subtracts the minimum value from the image and divides by the maximum value to normalize the image's values between 0 and 1

% Create a Gaussian filter with standard deviation of 3 pixels
filter_size = 7; % sets the size of the filter in both dimensions to 7
sigma = 3; % sets the standard deviation of the Gaussian distribution to 3
[filter, x0, y0] = fspecial('gaussian', [filter_size, filter_size], sigma); % creates a Gaussian filter with the specified size and standard deviation and stores it in the variables "filter", "x0", and "y0"

Now that we have our blurry image and a filter to help us restore it, let’s apply them using MATLAB’s `conv2` function. This will essentially “convolve” the two images together (hence the name deconvolution), resulting in an output with more detail:

% Apply the Gaussian filter to the blurry image and save it as a new variable called 'deblurred'
% The 'conv2' function convolves two images together, resulting in a deblurred output with more detail
deblurred = conv2(blurry_img, gaussian_filter, 'same');

And that’s it! You now have a deconvolved version of your original image. Of course, this is just one example of how to use MATLAB for signal processing tasks like deconvolution there are many other techniques and functions available depending on what you need to do. But hopefully this gives you an idea of the basic principles involved in restoring blurry images using math and code!