In this problem you will implement a spatial-domain low-pass filter using matlab and evaluate the difference between the filtered image and the original image using two quantitative metrics called
Given two \(N_1\) x \(N_2\) images \(x(n_1, n_2)\) and \(y(n_1, n_2)\)
\(MSE = \frac{1}{N_1 N_2} \sum_{n_1 = 1}^{N_1} \sum_{n_2 = 1}^{N_2} [x(n_1,n_2) - y(n_1,n_2)]^2\)
I tried to find if my stats book had mean standard error with a similar equation. I did not find it, but here is the wikipedia definition which does have some variables from stats, ie. sample. Considering its squared, we have \(\frac{1}{n}\) instead of \(\frac{1}{\sqrt{n}}\)
\(MSE = \frac{1}{N} \sum_{i = 1}^{n} [\hat{Y_i} - Y_i]^2\)
\(\hat{Y_i}\) is preditions or outcomes. Y is true values or input. n is number of samples in this case its the number of pixels. Since this is a 2d, he is using double summartion.
\(PSNR = 10 \log_{10} (\frac{MAX_I^2}{MSE})\)
Where \(MAX_I^2\) is the maximum possible pixel value of the images. For 8-bit gray-scale images this is 255.
For the submission, submit the PSNR result for the 3x3 followed by the 5x5 result. Use a space to seperate the results. Record digits to 2 decimal points.