November 2018
Intermediate to advanced
492 pages
12h 19m
English
Content preview from Hands-On Image Processing with Python
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Questions
- Implement histogram matching for colored RGB images.
- 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.
- 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.
- Use ModeFilter() from PIL for linear smoothing with an image. When is it useful?
- 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? ...
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