impemwnt in python program

Part I (10\%): Computing linear filters in scikit-image/python. - Read a grayscale image moon.png. - Filter the grayscale image with the following filters. - Display results. - Write your own code to implement the following filters: (You cannot use any built-in functions from any library for this.) NOTE: You may need to appropriately pad your input image so that the output image has the same shape as the supplied input. 1. Laplacian Filter 2. [0,0,0,0,0] [0,1,0,1,0] [0,0,0,1,0] 3. [0,0,0] [6,0,6] [0,0,0] 4. Compute image enhancement using a Laplacian filter. Use your result from 1. Expected output for Part I: 1. Laplace filtering Part II (10\%): 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? Expected output for Part II: Original Part III ( 30%) : 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. This section highlights how image processing involves iterative algorithms. At every iteration, your program - (a) blurs the entire damaged image with a Gaussian smoothing filter, (b) then, 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. Expected output for Part III: damaged image restored image EDGE DETECTION Part IV (25\%): Edges - Read the grayscale image ex2.jpg. - Display the image. - Compute the gradient of the image (both the horizontal derivative and vertical derivative) using the improved Sobel operators. suitably combining the horizontal and the vertical derivative images. Display the gradient magnitude image