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()

```

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

{"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"}]}