#REGULARIZATION FACTOR PIV IMAGEJ HOW TO#
#REGULARIZATION FACTOR PIV IMAGEJ INSTALL#
#REGULARIZATION FACTOR PIV IMAGEJ UPDATE#
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The lower the percentage is, the more the output image will eventually look like the input image. A higher percentage makes the algorithm quit earlier and makes the output image less defined. In other words, how far the algorithm is proceeded. Modification threshold is the lower boundary for percentage of modifications that improve the image. A higher regularization factor results in a greater dependency of image sparsity, and a lower regularization factor makes the log likelihood more important. Regularization factor indicates how important the log likelihood and image sparsity are compared to one another. Parameter explanation and usage:ĭark count rate per pixel of the camera used to record the data. If there are very few possible modifications left that could improve the image, the method for changing random pixels is altered, so the modifications become less extreme and the image is fine tuned. This check includes testing for the log likelihood of the new image, and the sparsity of the new image. A check is performed to test whether the random changes have improved the image.

The original image is blurred and random changes are made. The input image is reconstructed using the algorithm of the article ‘Imaging with a small number of photons’, by P. This option can be used to reconstruct the output image created by ‘Process Photon Images’. All pixels below the threshold value are set to 0, and the remaining pixels are scaled to new values. This option can be used to filter noise from the output image created by ‘Process Photon Images’, and optionally prepare for ‘Reconstruct Image’. This requires more input images and bigger light blobs in those input images to work successfully. Sub-pixel resolution uses the accurate method to calculate the midpoints but creates an output image of a higher resolution (height * 2 and width * 2). Simple uses the lightest pixels found as coordinates for the output image.Īccurate improves on ‘Simple’ by also checking the pixels surrounding the lightest pixel to calculate a more accurate midpoint. There are 3 different methods to choose from to calculate the coordinates of the exact midpoints of the light blobs in the images. This output image might optionally be improved by ‘Threshold Photon Count’ and/or ‘Reconstruct Image’. This option can be used to process a stack of single photon event data (images containing single photon events), and create one high resolution output image. The opened virtual stack can be used as input for ‘Process Photon Images’. This option can be used to open all TIFF files in a directory, and the directories below, as virtual stack.

#REGULARIZATION FACTOR PIV IMAGEJ UPDATE#

If you have enabled the update site, you will automatically receive updates for the plugin when their released. When correctly installed, you’ll now have Plugins › Photon Image Processor available.

#REGULARIZATION FACTOR PIV IMAGEJ DOWNLOAD#

This will prompt you to download the plugin the next time ImageJ searches for updates.Īfter you installed the plugin, restart ImageJ.

Users can enable the update site ‘PhotonImaging’ via Update… › Manage update sites.

#REGULARIZATION FACTOR PIV IMAGEJ INSTALL#

For developers, who cloned this project and build a jar file via maven, you can install the plugin in ImageJ via Plugins › Install PlugIn…. There are two ways of installing the plugin. The source has been written in IntelliJ IDEA 2016 and the project uses Maven for package management and building. Make sure that ImageJ is installed on your Windows/Mac/Linux device and that ImageJ uses Java 8. This plugin requires at least Java 8 to function. This plugin for ImageJ is able to process single photon event data, by locating the center point of each photon and create a combined greyscale image with all found photons per pixel mapped to the correct pixel value.

#REGULARIZATION FACTOR PIV IMAGEJ HOW TO#

If you’d like to help, check out the how to help guide! Photon ImagingĪuthors: Lonneke Scheffer & Wout van Helvoirt The content of this page has not been vetted since shifting away from MediaWiki.

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