Hands-On Image Processing with Python

Questions

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? ...