Python Image Processing Cookbook

How to do it...

Perform the following steps to denoise an image with the implementation of Perona-Malik anisotropic diffusion:

1. First, read the cameraman grayscale image from disk and add random Gaussian noise (with a variance of 0.025) to it. Clip the noisy image to have values strictly between 0 and 1 (inclusive):

img = img_as_float(imread('images/cameraman.png'))noisy = random_noise(img, var=0.005)noisy = np.clip(noisy, 0, 1)

2. Plot the original and noisy images:

plt.figure(figsize=(15,18))plt.gray()plt.subplots_adjust(0,0,1,1,0.05,0.05)plt.subplot(221), plt.imshow(img), plt.axis('off'), plt.title('Original', size=20)plt.subplot(222), plt.imshow(noisy), plt.axis('off'), plt.title('Noisy', size=20)

3. Apply anisotropic diffusion (according ...