prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Existing "inpainting" gallery example could use a better (more specific) title.
  Creating this issue so we don't lose track of what's been discussed in the conversation _Originally posted by @lagru in https://github.com/scikit-image/scikit-image/pull/6853#discussion_r1149741067_
  
  > @mkcor, just wondering how this relates to [our existing inpainting example ](https://scikit-image.org/docs/dev/auto_examples/filters/plot_inpaint.html#sphx-glr-auto-examples-filters-plot-inpaint-py). I am assuming that the main benefit here is that it's a real world use case?
  
  [...]
  
  > Which prompts the idea that we should update the title of the existing example, so it's less generic than just "inpainting."
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `doc/examples/filters/plot_inpaint.py`
  Content:
  ```
  1 """
  2 ===========
  3 Inpainting
  4 ===========
  5 Inpainting [1]_ is the process of reconstructing lost or deteriorated
  6 parts of images and videos.
  7 
  8 The reconstruction is supposed to be performed in fully automatic way by
  9 exploiting the information presented in non-damaged regions.
  10 
  11 In this example, we show how the masked pixels get inpainted by
  12 inpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_ [4]_.
  13 
  14 .. [1]  Wikipedia. Inpainting
  15         https://en.wikipedia.org/wiki/Inpainting
  16 .. [2]  Wikipedia. Biharmonic equation
  17         https://en.wikipedia.org/wiki/Biharmonic_equation
  18 .. [3]  S.B.Damelin and N.S.Hoang. "On Surface Completion and Image
  19         Inpainting by Biharmonic Functions: Numerical Aspects",
  20         International Journal of Mathematics and Mathematical Sciences,
  21         Vol. 2018, Article ID 3950312
  22         :DOI:`10.1155/2018/3950312`
  23 .. [4]  C. K. Chui and H. N. Mhaskar, MRA Contextual-Recovery Extension of
  24         Smooth Functions on Manifolds, Appl. and Comp. Harmonic Anal.,
  25         28 (2010), 104-113,
  26         :DOI:`10.1016/j.acha.2009.04.004`
  27 """
  28 
  29 import numpy as np
  30 import matplotlib.pyplot as plt
  31 
  32 from skimage import data
  33 from skimage.morphology import disk, binary_dilation
  34 from skimage.restoration import inpaint
  35 
  36 image_orig = data.astronaut()
  37 
  38 # Create mask with six block defect regions
  39 mask = np.zeros(image_orig.shape[:-1], dtype=bool)
  40 mask[20:60, 0:20] = 1
  41 mask[160:180, 70:155] = 1
  42 mask[30:60, 170:195] = 1
  43 mask[-60:-30, 170:195] = 1
  44 mask[-180:-160, 70:155] = 1
  45 mask[-60:-20, 0:20] = 1
  46 
  47 # add a few long, narrow defects
  48 mask[200:205, -200:] = 1
  49 mask[150:255, 20:23] = 1
  50 mask[365:368, 60:130] = 1
  51 
  52 # add randomly positioned small point-like defects
  53 rstate = np.random.default_rng(0)
  54 for radius in [0, 2, 4]:
  55     # larger defects are less common
  56     thresh = 3 + 0.25 * radius  # make larger defects less common
  57     tmp_mask = rstate.standard_normal(image_orig.shape[:-1]) > thresh
  58     if radius > 0:
  59         tmp_mask = binary_dilation(tmp_mask, disk(radius, dtype=bool))
  60     mask[tmp_mask] = 1
  61 
  62 # Apply defect mask to the image over the same region in each color channel
  63 image_defect = image_orig * ~mask[..., np.newaxis]
  64 
  65 image_result = inpaint.inpaint_biharmonic(image_defect, mask, channel_axis=-1)
  66 
  67 fig, axes = plt.subplots(ncols=2, nrows=2)
  68 ax = axes.ravel()
  69 
  70 ax[0].set_title('Original image')
  71 ax[0].imshow(image_orig)
  72 
  73 ax[1].set_title('Mask')
  74 ax[1].imshow(mask, cmap=plt.cm.gray)
  75 
  76 ax[2].set_title('Defected image')
  77 ax[2].imshow(image_defect)
  78 
  79 ax[3].set_title('Inpainted image')
  80 ax[3].imshow(image_result)
  81 
  82 for a in ax:
  83     a.axis('off')
  84 
  85 fig.tight_layout()
  86 plt.show()
  87
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/doc/examples/filters/plot_inpaint.py b/doc/examples/filters/plot_inpaint.py
  --- a/doc/examples/filters/plot_inpaint.py
  +++ b/doc/examples/filters/plot_inpaint.py
  @@ -1,15 +1,16 @@
   """
  -===========
  -Inpainting
  -===========
  +===============================
  +Fill in defects with inpainting
  +===============================
  +
   Inpainting [1]_ is the process of reconstructing lost or deteriorated
   parts of images and videos.
   
  -The reconstruction is supposed to be performed in fully automatic way by
  -exploiting the information presented in non-damaged regions.
  +The reconstruction (restoration) is performed in an automatic way by
  +exploiting the information present in non-damaged regions.
   
  -In this example, we show how the masked pixels get inpainted by
  -inpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_ [4]_.
  +In this example, we show how the masked pixels get inpainted using an
  +inpainting algorithm based on the biharmonic equation [2]_ [3]_ [4]_.
   
   .. [1]  Wikipedia. Inpainting
           https://en.wikipedia.org/wiki/Inpainting
  @@ -44,12 +45,12 @@
   mask[-180:-160, 70:155] = 1
   mask[-60:-20, 0:20] = 1
   
  -# add a few long, narrow defects
  +# Add a few long, narrow defects
   mask[200:205, -200:] = 1
   mask[150:255, 20:23] = 1
   mask[365:368, 60:130] = 1
   
  -# add randomly positioned small point-like defects
  +# Add randomly positioned small point-like defects
   rstate = np.random.default_rng(0)
   for radius in [0, 2, 4]:
       # larger defects are less common
  
verification_info: {"golden_diff": "diff --git a/doc/examples/filters/plot_inpaint.py b/doc/examples/filters/plot_inpaint.py\n--- a/doc/examples/filters/plot_inpaint.py\n+++ b/doc/examples/filters/plot_inpaint.py\n@@ -1,15 +1,16 @@\n \"\"\"\n-===========\n-Inpainting\n-===========\n+===============================\n+Fill in defects with inpainting\n+===============================\n+\n Inpainting [1]_ is the process of reconstructing lost or deteriorated\n parts of images and videos.\n \n-The reconstruction is supposed to be performed in fully automatic way by\n-exploiting the information presented in non-damaged regions.\n+The reconstruction (restoration) is performed in an automatic way by\n+exploiting the information present in non-damaged regions.\n \n-In this example, we show how the masked pixels get inpainted by\n-inpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_ [4]_.\n+In this example, we show how the masked pixels get inpainted using an\n+inpainting algorithm based on the biharmonic equation [2]_ [3]_ [4]_.\n \n .. [1]  Wikipedia. Inpainting\n         https://en.wikipedia.org/wiki/Inpainting\n@@ -44,12 +45,12 @@\n mask[-180:-160, 70:155] = 1\n mask[-60:-20, 0:20] = 1\n \n-# add a few long, narrow defects\n+# Add a few long, narrow defects\n mask[200:205, -200:] = 1\n mask[150:255, 20:23] = 1\n mask[365:368, 60:130] = 1\n \n-# add randomly positioned small point-like defects\n+# Add randomly positioned small point-like defects\n rstate = np.random.default_rng(0)\n for radius in [0, 2, 4]:\n     # larger defects are less common\n", "issue": "Existing \"inpainting\" gallery example could use a better (more specific) title.\nCreating this issue so we don't lose track of what's been discussed in the conversation _Originally posted by @lagru in https://github.com/scikit-image/scikit-image/pull/6853#discussion_r1149741067_\r\n\r\n> @mkcor, just wondering how this relates to [our existing inpainting example ](https://scikit-image.org/docs/dev/auto_examples/filters/plot_inpaint.html#sphx-glr-auto-examples-filters-plot-inpaint-py). I am assuming that the main benefit here is that it's a real world use case?\r\n\r\n[...]\r\n\r\n> Which prompts the idea that we should update the title of the existing example, so it's less generic than just \"inpainting.\"\n", "before_files": [{"content": "\"\"\"\n===========\nInpainting\n===========\nInpainting [1]_ is the process of reconstructing lost or deteriorated\nparts of images and videos.\n\nThe reconstruction is supposed to be performed in fully automatic way by\nexploiting the information presented in non-damaged regions.\n\nIn this example, we show how the masked pixels get inpainted by\ninpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_ [4]_.\n\n.. [1]  Wikipedia. Inpainting\n        https://en.wikipedia.org/wiki/Inpainting\n.. [2]  Wikipedia. Biharmonic equation\n        https://en.wikipedia.org/wiki/Biharmonic_equation\n.. [3]  S.B.Damelin and N.S.Hoang. \"On Surface Completion and Image\n        Inpainting by Biharmonic Functions: Numerical Aspects\",\n        International Journal of Mathematics and Mathematical Sciences,\n        Vol. 2018, Article ID 3950312\n        :DOI:`10.1155/2018/3950312`\n.. [4]  C. K. Chui and H. N. Mhaskar, MRA Contextual-Recovery Extension of\n        Smooth Functions on Manifolds, Appl. and Comp. Harmonic Anal.,\n        28 (2010), 104-113,\n        :DOI:`10.1016/j.acha.2009.04.004`\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom skimage import data\nfrom skimage.morphology import disk, binary_dilation\nfrom skimage.restoration import inpaint\n\nimage_orig = data.astronaut()\n\n# Create mask with six block defect regions\nmask = np.zeros(image_orig.shape[:-1], dtype=bool)\nmask[20:60, 0:20] = 1\nmask[160:180, 70:155] = 1\nmask[30:60, 170:195] = 1\nmask[-60:-30, 170:195] = 1\nmask[-180:-160, 70:155] = 1\nmask[-60:-20, 0:20] = 1\n\n# add a few long, narrow defects\nmask[200:205, -200:] = 1\nmask[150:255, 20:23] = 1\nmask[365:368, 60:130] = 1\n\n# add randomly positioned small point-like defects\nrstate = np.random.default_rng(0)\nfor radius in [0, 2, 4]:\n    # larger defects are less common\n    thresh = 3 + 0.25 * radius  # make larger defects less common\n    tmp_mask = rstate.standard_normal(image_orig.shape[:-1]) > thresh\n    if radius > 0:\n        tmp_mask = binary_dilation(tmp_mask, disk(radius, dtype=bool))\n    mask[tmp_mask] = 1\n\n# Apply defect mask to the image over the same region in each color channel\nimage_defect = image_orig * ~mask[..., np.newaxis]\n\nimage_result = inpaint.inpaint_biharmonic(image_defect, mask, channel_axis=-1)\n\nfig, axes = plt.subplots(ncols=2, nrows=2)\nax = axes.ravel()\n\nax[0].set_title('Original image')\nax[0].imshow(image_orig)\n\nax[1].set_title('Mask')\nax[1].imshow(mask, cmap=plt.cm.gray)\n\nax[2].set_title('Defected image')\nax[2].imshow(image_defect)\n\nax[3].set_title('Inpainted image')\nax[3].imshow(image_result)\n\nfor a in ax:\n    a.axis('off')\n\nfig.tight_layout()\nplt.show()\n", "path": "doc/examples/filters/plot_inpaint.py"}], "after_files": [{"content": "\"\"\"\n===============================\nFill in defects with inpainting\n===============================\n\nInpainting [1]_ is the process of reconstructing lost or deteriorated\nparts of images and videos.\n\nThe reconstruction (restoration) is performed in an automatic way by\nexploiting the information present in non-damaged regions.\n\nIn this example, we show how the masked pixels get inpainted using an\ninpainting algorithm based on the biharmonic equation [2]_ [3]_ [4]_.\n\n.. [1]  Wikipedia. Inpainting\n        https://en.wikipedia.org/wiki/Inpainting\n.. [2]  Wikipedia. Biharmonic equation\n        https://en.wikipedia.org/wiki/Biharmonic_equation\n.. [3]  S.B.Damelin and N.S.Hoang. \"On Surface Completion and Image\n        Inpainting by Biharmonic Functions: Numerical Aspects\",\n        International Journal of Mathematics and Mathematical Sciences,\n        Vol. 2018, Article ID 3950312\n        :DOI:`10.1155/2018/3950312`\n.. [4]  C. K. Chui and H. N. Mhaskar, MRA Contextual-Recovery Extension of\n        Smooth Functions on Manifolds, Appl. and Comp. Harmonic Anal.,\n        28 (2010), 104-113,\n        :DOI:`10.1016/j.acha.2009.04.004`\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom skimage import data\nfrom skimage.morphology import disk, binary_dilation\nfrom skimage.restoration import inpaint\n\nimage_orig = data.astronaut()\n\n# Create mask with six block defect regions\nmask = np.zeros(image_orig.shape[:-1], dtype=bool)\nmask[20:60, 0:20] = 1\nmask[160:180, 70:155] = 1\nmask[30:60, 170:195] = 1\nmask[-60:-30, 170:195] = 1\nmask[-180:-160, 70:155] = 1\nmask[-60:-20, 0:20] = 1\n\n# Add a few long, narrow defects\nmask[200:205, -200:] = 1\nmask[150:255, 20:23] = 1\nmask[365:368, 60:130] = 1\n\n# Add randomly positioned small point-like defects\nrstate = np.random.default_rng(0)\nfor radius in [0, 2, 4]:\n    # larger defects are less common\n    thresh = 3 + 0.25 * radius  # make larger defects less common\n    tmp_mask = rstate.standard_normal(image_orig.shape[:-1]) > thresh\n    if radius > 0:\n        tmp_mask = binary_dilation(tmp_mask, disk(radius, dtype=bool))\n    mask[tmp_mask] = 1\n\n# Apply defect mask to the image over the same region in each color channel\nimage_defect = image_orig * ~mask[..., np.newaxis]\n\nimage_result = inpaint.inpaint_biharmonic(image_defect, mask, channel_axis=-1)\n\nfig, axes = plt.subplots(ncols=2, nrows=2)\nax = axes.ravel()\n\nax[0].set_title('Original image')\nax[0].imshow(image_orig)\n\nax[1].set_title('Mask')\nax[1].imshow(mask, cmap=plt.cm.gray)\n\nax[2].set_title('Defected image')\nax[2].imshow(image_defect)\n\nax[3].set_title('Inpainted image')\nax[3].imshow(image_result)\n\nfor a in ax:\n    a.axis('off')\n\nfig.tight_layout()\nplt.show()\n", "path": "doc/examples/filters/plot_inpaint.py"}]}