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Make the sample image download links clearer
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docs/ops/doc/examples/deconvolution.rst

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@@ -8,7 +8,12 @@ The SciJava Ops framework currently supports the standard RL algorithm as well a
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algorithm, which utilizes a regularization factor to limit the noise amplified by the RL algorithm :sup:`1`. Typically,
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the RLTV algorithm returns improved axial and lateral resolution when compared to RL.
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You can download the 3D HeLa cell nuclus dataset `here`_.
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You can download the 3D HeLa cell nucleus dataset here:
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.. admonition:: Download
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:class: note
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`hela_nucleus.tif`_
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.. figure:: https://media.scijava.org/scijava-ops/1.0.0/rltv_example_1.gif
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.. _`Dey et. al, Micros Res Tech 2006`: https://pubmed.ncbi.nlm.nih.gov/16586486/
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.. _`Gibson & Lanni, JOSA 1992`: https://pubmed.ncbi.nlm.nih.gov/1738047/
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.. _`here`: https://media.scijava.org/scijava-ops/1.0.0/hela_nucleus.tif
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.. _`hela_nucleus.tif`: https://media.scijava.org/scijava-ops/1.0.0/hela_nucleus.tif
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.. _`script parameters`: https://imagej.net/scripting/parameters

docs/ops/doc/examples/opencv_denoise.rst

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@@ -9,7 +9,12 @@ a pixel of interest (*e.g.* a 5x5 patch) with patches from other pixels from the
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patches are then averaged to eliminate gaussian noise, without the requirement of additional
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images for comparison.
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The sample data for this example can be downloaded `here`_.
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The sample data for this example can be downloaded here:
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.. admonition:: Download
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:class: note
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`opencv_denoise_16bit.tif`_
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.. figure:: https://media.scijava.org/scijava-ops/1.0.0/opencv_denoise_example_1.png
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.. _`script parameters`: https://imagej.net/scripting/parameters
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.. _`OpenCV library`: https://docs.opencv.org/4.x/d5/d69/tutorial_py_non_local_means.html
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.. _`here`: https://media.scijava.org/scijava-ops/1.0.0/opencv_denoise_16bit.tif
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.. _`opencv_denoise_16bit.tif`: https://media.scijava.org/scijava-ops/1.0.0/opencv_denoise_16bit.tif

docs/ops/doc/examples/saca.rst

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@@ -17,7 +17,12 @@ can be computed easily by using the *Z*-score heatmap with the ``stats.pnorm`` O
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significantly colocalized. This example script takes advantage of this feature of the SACA framework and utilizes the significant pixel
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mask as a region of interest to compute Pearson's :sup:`4` and Li's :sup:`5` colocalization coefficients.
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You can download the colocalization dataset `here`_.
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You can download the colocalization dataset here:
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.. admonition:: Download
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:class: note
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`hela_hiv_gag_ms2_mcherry.tif`_
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.. figure:: https://media.scijava.org/scijava-ops/1.0.0/saca_input.png
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.. _`Becker and Sherer, JVI 2017`: https://pubmed.ncbi.nlm.nih.gov/28053097/
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.. _`Wang et. al, IEEE 2019`: https://ieeexplore.ieee.org/abstract/document/8681436
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.. _`Stockley et. al, Bacteriophage 2016`: https://pubmed.ncbi.nlm.nih.gov/27144089/
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.. _`here`: https://media.scijava.org/scijava-ops/1.0.0/hela_hiv_gag_ms2_mcherry.tif
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.. _`hela_hiv_gag_ms2_mcherry.tif`: https://media.scijava.org/scijava-ops/1.0.0/hela_hiv_gag_ms2_mcherry.tif
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.. _`script parameters`: https://imagej.net/scripting/parameters

docs/ops/doc/examples/volume_labeling.rst

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5. Measure the 3D geometry of the puncta and nuclear regions.
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6. Display results in two tables (puncta and nuclear).
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To use the script in the example first download the sample dataset `here`_. Next, open Fiji, the sample image and this scipt. Click **Run** to create the script parameter GUI,
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To use the script in the example, first download the sample dataset:
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.. admonition:: Download
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:class: note
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`hela_hiv_vif.tif`_
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Next, open Fiji, the sample image and this script. Click **Run** to create the script parameter GUI,
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where you can customize some values to your own data, such as the channel names, channel position and image calibration values.
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.. figure:: https://media.scijava.org/scijava-ops/1.1.0/labeling_mesh_example_dialog.png
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ui.show("{} results table".format(ch_a_name), ab_table)
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ui.show("{} results table".format(ch_b_name), b_table)
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.. _`here`: https://media.scijava.org/scijava-ops/1.0.0/hela_hiv_vif.tif
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.. _`hela_hiv_vif.tif`: https://media.scijava.org/scijava-ops/1.0.0/hela_hiv_vif.tif

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