1. Use the skimage.filters module's unsharp_mask() function with different values of the radius and amount parameters to sharpen an image.
2. Use the PIL ImageFilter module's UnsharpMask() function with different values of the radius and percent parameters to sharpen an image.
3. Sharpen a color (RGB) image using the sharpen kernel [[0, -1, 0], [-1, 5, -1], [0, -1, 0]]. (Hint: use SciPy signal module's convolve2d() function for each of the color channels one by one.)
4. With the SciPy ndimage module, sharpen a color image directly (without sharpening individual color channels one by one).
5. Compute and display a Gaussian pyramid with the lena gray-scale input image using the skimage.transform module's pyramid_laplacian() function.
   

6. Construct the Gaussian pyramid with the reduce() function of the transform module of scikit-image and Laplacian pyramid from the Gaussian pyramid and expand() function, with the algorithm discussed.
7. Compute the Laplacian pyramid for an image and construct the original image from it.
8. Show that the LoG and DoG kernels look like a Mexican hat in a 3D surface plot.
9. Implement Marr and Hildreth's zero-crossing algorithm for edge detection with LoG.
10. Implement the non-max suppression algorithm to thin the edges in a gradient magnitude image.