Notebook name: gamma_filtering_tool.ipynb
This notebook will allow you to compare data loaded without any correction against data loaded with gamma filtering on. You will be able to change the gamma filtering coefficient to optimize the cleaning of the gammas without dammaging the images.
If you need help accessing this notebook, check the How To > Start the python notebooks tutorial.
Check the full tutorial here
Simply select all the images you want to work on.
Moving the mouse over the raw or filtered image will give you its value in the status bar (bottom left) of the UI. Also any zoom or pan transformation in one of the image will be reproduced in the other image.
After changing the gamma filtering coefficient and hitting ENTER, the entire stack of data will be reloaded using the new filter coefficient. The table will show you the new percentage and number of pixels cleaned. The gamma filtered plot will be refreshed to display the new cleaned selected image.
If you wonder how the gamma filtering algorithm works and what is the meaning behind this magic gamma filtering coefficient
Here is the workflow:
A newer version of the UI offers the histogram of the images before and after filtering. This helps figuring out where the gamma are located.