This project aims to detect and filter impulse noise from images using differential rank impulse detection followed by median filtering. Specifically, the student must design a Matlab function that takes an image, rank interval length r, and threshold s as inputs to detect noisy pixels and apply median filtering only to those pixels. The function should handle border effects using mirroring. The student will then add random impulse noise to a test image, filter it using their function with different parameter values, and report the best filtering result based on RMSE/PSNR metrics in a brief technical report.

1. CS-467 Image processing and Computer Vision
Course Project 4
Goals:
1) To learn how to detect impulse noise and then filter it based on the detection results.
1. Design the following Matlab function:
a) A function, which utilizes differential rank impulse detection followed by median filtering only
of those pixels, which were detected as noisy. Do not forget to take care of border effect and to
extend an image over its borders using mirroring.
The function shall accept an image (matrix), the length of a rank interval r, and threshold s (see
slide 17 of Lecture-6) as parameters and return a filtered image (matrix).
2. Choose an image ( , )f x y
3. Generate random impulse noise ( , )x yη with the corruption rate 0.2 using the function, which you
designed earlier (Project 3), and save a noisy picture.
4. Filter your noisy image using the median filter with the differential rank impulse detector. Use the
function, which you designed. You may apply this filter more than 1 time, if necessary. Try different
values of interval r, and s and find the values giving the best filtering result in terms of RMSE/PSNR.
5. Save your best resulting image.
6. Prepare a brief technical report containing your RMSE/PSNR values.