Call add$\_$Gnoise$()$ with different levels of noise, such as $5,10,15,$ and $20.$

Display the original images and their corresponding noisy images. Something like this.

Utilize the function filter$\_$image$()$ to apply the three mentioned filters to the noisy images.

Display the Boat noisy image with different levels of noise, and the filtered images as follows $($only some images are shown here$)$:

Lena $)=(5$ Lena $)=(10$ Lena $)=(15$ Lena $)=(15$

Redo $10$ for other cases.

Call add$\_$SPnoise$().$

Redo questions $8,9,$ and $10$ and $11.$

Develop a Python function called smooth$\_$image$()$ to apply Gaussian smoothing to the original Mandrill image using a kernel size parameter to control the degree of smoothing.

Construct a Python function named laplace$\_$sharpening$()$ to sharpen the smoothed Mandrill image by applying Laplace sharpening, with a kernel size parameter determining the extent of sharpening.

Present various smoothed and sharpened images, showcasing different parameter values.