1. Implement histogram matching for colored RGB images.
2. Use the equalize() function from skimage.filters.rank to implement local histogram equalization and compare it with the global histogram equalization from skimage.exposure with a grayscale image.
3. Implement Floyd-Steinberg error-diffusion dithering using the algorithm described here https://en.wikipedia.org/wiki/Floyd%E2%80%93Steinberg_dithering and convert a grayscale image into a binary image.
4. Use ModeFilter() from PIL for linear smoothing with an image. When is it useful?
5. Show an image that can be recovered from a few noisy images obtained by adding random Gaussian noise to the original image by simply taking the average of the noisy images. Does taking the median also work?