Python Image Filtering
Tags: Python, Scikit-image, Gaussian filter, Image Processing
Part I (25%) Computing linear filters in scikit-image/python.
- Read a grayscale image moon.png.
- Filter the grayscale image with the following filters.
- Display results.
- Create a 3-by-3 matrix and write your own code to implement these filters: (You cannot use any built in functions from any library for this.)
1) Laplacian Filter
4) compute Im + (Im - Im average_filter(mean filter)) #where is a convolution operation and Im is moon.png.
Part II (15%) Median and Gaussian filters
- Read noisy.jpg corrupted with salt and pepper noise.
- Apply a median filter to remove the noise.
- Apply a Gaussian filter to the same noisy image.
- You can use any scikit-image functions you like.
- Which filter was more successful?
Part III (40%) An application of filtering in scikit-image: Simple image inpainting.
Write a program in scikit-image/Python to accomplish a simple image inpainting. This example and demo were shown in the lecture.
Use damage_cameraman.png and damage_mask.png.
It is an iterative algorithm. At every iteration, your program (a) blurs the entire damaged image with a Gaussian smoothing filter; then (b) with help of the mask image, restores only the undamaged pixels. Repeat these two steps (a) and (b) a few times until all damaged pixels are infilled.