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Improve opencv denoise use case
This commit adds more details about the denoise operation and describes what each of the parameters do. Additional this commit adds an image of the expected output with the recommended starting values.
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‎docs/ops/doc/examples/opencv_denoise.rst‎

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Fast Non-Local Means Denoise with OpenCV
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========================================
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In this example we will use a denoise algorithm defined in an `external libray`_: OpenCV.
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In this example we will use a denoise algorithm from the external `OpenCV libray`_.
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This method progressively scans neighborhoods of an image looking for repeated search template. These
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repeated areas can then be averaged to eliminate gaussian noise, without the requirement of additional
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This method progressively scans through an image's pixels, comparing a patch centered around
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a pixel of interest (*e.g.* a 5x5 patch) with patches from other pixels from the image. These
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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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Sample data can be found in the `images folder`_.
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SciJava Ops via Fiji's sripting engine with `script parameters`_:
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The sample data for this example can be downloaded `here`_.
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.. figure:: https://media.imagej.net/scijava-ops/1.0.0/opencv_denoise_example_1.png
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Results of OpenCV's non-local means denoise algorithm with the sample data.
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Denoise parameter descriptions
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==============================
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+-----------------+-------------------------------------------------------+-------+
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| Parameter | Description | Value |
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+=================+=======================================================+=======+
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| Filter strength | Controls the decay in patch weights as a function |4 |
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| | | |
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| | of *distance* between patches. The *distance* | |
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| | | |
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| | between patches is a measure of how similar they | |
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| | | |
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| | are. Large filter strength values allow more distant | |
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| | | |
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| | (*i.e.* dissimilar) patches to have more influence on | |
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| | | |
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| | the denoise output. | |
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+-----------------+-------------------------------------------------------+-------+
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| Patch size | The size of the patches/blocks from the input image |7 |
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| | | |
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| | to be compared. Patch sizes should be odd | |
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| | | |
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| | (default=7). | |
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+-----------------+-------------------------------------------------------+-------+
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| Search size | The size of the area in the input image to search |21 |
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| | | |
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| | for similar patches to compare. Search sizes | |
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| | | |
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| | should be odd (default=21). | |
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+-----------------+-------------------------------------------------------+-------+
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SciJava Ops via Fiji's scripting engine with `script parameters`_:
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.. tabs::
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.. code-tab:: scijava-groovy
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#@ OpEnvironment ops
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#@ ImgPlus img
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#@ Integer (label="strength:", value=4.0) strength
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#@ Integer (label="template size:", value=7) template
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#@ Integer (label="search size:", value=21) search
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#@ Integer (label="Filter strength:", value=4) strength
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#@ Integer (label="Patch size:", value=7) patch
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#@ Integer (label="Search size:", value=21) search
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#@output ImgPlus result
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import net.imglib2.type.numeric.integer.UnsignedByteType
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output = img8bit.copy()
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// Run the denoise op
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ops.quaternary("filter.denoise").input(img8bit, strength, template, search).output(output).compute()
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ops.quaternary("filter.denoise").input(img8bit, strength, patch, search).output(output).compute()
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// Return the denoised image
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result = output
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#@ OpEnvironment ops
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#@ ImgPlus img
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#@ Integer (label="strength:", value=4.0) strength
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#@ Integer (label="template size:", value=7) template
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#@ Integer (label="search size:", value=21) search
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#@ Integer (label="Filter strength:", value=4) strength
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#@ Integer (label="Patch size:", value=7) patch
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#@ Integer (label="Search size:", value=21) search
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#@output ImgPlus result
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from net.imglib2.type.numeric.integer import UnsignedByteType
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output = img8bit.copy()
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# Run the denoise op
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ops.quaternary("filter.denoise").input(img8bit, strength, template, search).output(output).compute()
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ops.quaternary("filter.denoise").input(img8bit, strength, patch, search).output(output).compute()
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# Return the denoised image
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result = output
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.. _`script parameters`: https://imagej.net/scripting/parameters
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.. _`external libray`: https://docs.opencv.org/4.x/d5/d69/tutorial_py_non_local_means.html
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.. _`images folder`: https://github.com/scijava/incubator/tree/main/docs/ops/images/sample_16bit_T24.png
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.. _`OpenCV libray`: https://docs.opencv.org/4.x/d5/d69/tutorial_py_non_local_means.html
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.. _`here`: https://media.imagej.net/scijava-ops/1.0.0/opencv_denoise_16bit.png

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