old_contents: """
  =============================
  Denoising the picture of Lena
  =============================
  
  In this example, we denoise a noisy version of the picture of Lena using the
  total variation and bilateral denoising filter.
  
  These algorithms typically produce "posterized" images with flat domains
  separated by sharp edges. It is possible to change the degree of posterization
  by controlling the tradeoff between denoising and faithfulness to the original
  image.
  
  Total variation filter
  ----------------------
  
  The result of this filter is an image that has a minimal total variation norm,
  while being as close to the initial image as possible. The total variation is
  the L1 norm of the gradient of the image.
  
  Bilateral filter
  ----------------
  
  A bilateral filter is an edge-preserving and noise reducing filter. It averages
  pixels based on their spatial closeness and radiometric similarity.
  
  """
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from skimage import data, color, img_as_float
  from skimage.filter import denoise_tv, denoise_bilateral
  
  lena = color.rgb2gray(img_as_float(data.lena()))
  lena = lena[220:300, 220:320]
  
  noisy = lena + 0.5 * lena.std() * np.random.random(lena.shape)
  noisy = np.clip(noisy, 0, 1)
  
  fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
  
  plt.gray()
  
  ax[0, 0].imshow(noisy)
  ax[0, 0].axis('off')
  ax[0, 0].set_title('noisy')
  ax[0, 1].imshow(denoise_tv(noisy, weight=40))
  ax[0, 1].axis('off')
  ax[0, 1].set_title('TV')
  ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.03, sigma_spatial=15))
  ax[0, 2].axis('off')
  ax[0, 2].set_title('Bilateral')
  
  ax[1, 0].imshow(denoise_tv(noisy, weight=20))
  ax[1, 0].axis('off')
  ax[1, 0].set_title('(more) TV')
  ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.06, sigma_spatial=15))
  ax[1, 1].axis('off')
  ax[1, 1].set_title('(more) Bilateral')
  ax[1, 2].imshow(lena)
  ax[1, 2].axis('off')
  ax[1, 2].set_title('original')
  
  fig.subplots_adjust(wspace=0.02, hspace=0.2,
                      top=0.9, bottom=0.05, left=0, right=1)
  
  plt.show()
  
new_contents: """
  =============================
  Denoising the picture of Lena
  =============================
  
  In this example, we denoise a noisy version of the picture of Lena using the
  total variation and bilateral denoising filter.
  
  These algorithms typically produce "posterized" images with flat domains
  separated by sharp edges. It is possible to change the degree of posterization
  by controlling the tradeoff between denoising and faithfulness to the original
  image.
  
  Total variation filter
  ----------------------
  
  The result of this filter is an image that has a minimal total variation norm,
  while being as close to the initial image as possible. The total variation is
  the L1 norm of the gradient of the image.
  
  Bilateral filter
  ----------------
  
  A bilateral filter is an edge-preserving and noise reducing filter. It averages
  pixels based on their spatial closeness and radiometric similarity.
  
  """
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from skimage import data, color, img_as_float
  from skimage.filter import denoise_tv_chambolle, denoise_bilateral
  
  lena = img_as_float(data.lena())
  lena = lena[220:300, 220:320]
  
  noisy = lena + 0.6 * lena.std() * np.random.random(lena.shape)
  noisy = np.clip(noisy, 0, 1)
  
  fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
  
  plt.gray()
  
  ax[0, 0].imshow(noisy)
  ax[0, 0].axis('off')
  ax[0, 0].set_title('noisy')
  ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True))
  ax[0, 1].axis('off')
  ax[0, 1].set_title('TV')
  ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15))
  ax[0, 2].axis('off')
  ax[0, 2].set_title('Bilateral')
  
  ax[1, 0].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True))
  ax[1, 0].axis('off')
  ax[1, 0].set_title('(more) TV')
  ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.1, sigma_spatial=15))
  ax[1, 1].axis('off')
  ax[1, 1].set_title('(more) Bilateral')
  ax[1, 2].imshow(lena)
  ax[1, 2].axis('off')
  ax[1, 2].set_title('original')
  
  fig.subplots_adjust(wspace=0.02, hspace=0.2,
                      top=0.9, bottom=0.05, left=0, right=1)
  
  plt.show()
  
subject: Update denoising example