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https://github.com/weisongzhao/percentile_normalization.imagej
https://github.com/weisongzhao/percentile_normalization.imagej
Last synced: 25 days ago
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- Host: GitHub
- URL: https://github.com/weisongzhao/percentile_normalization.imagej
- Owner: WeisongZhao
- Created: 2024-03-10T07:24:34.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-05-07T17:19:03.000Z (6 months ago)
- Last Synced: 2024-05-08T09:42:24.621Z (6 months ago)
- Language: Java
- Size: 16.9 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Percentile_normalization Plugin for imagej
## Introduction
*This plugin is developed for SN2N, a self-inspired learning to denoise method for live-cell super-resolution microscopy.*
- **Preprint:** [Liying Qu et al. Self-inspired learning to denoise for live-cell super-resolution microscopy, bioRxiv (2024).](https://doi.org/10.1101/2024.01.23.576521)
- **Code avaliable:** https://github.com/WeisongZhao/SN2N
- For the ultralow SNR data with ultrahigh baseline signal and a number of hot pixels, we adapted the routinely used percentile normalization before the data generation step to remove the smooth background or hot pixels.
*This ImageJ plugin offers real-time execution of Image Percentile Normalization. Users can directly remove the ultra-strong baseline signal before training using this ready-to-use plugin.*
## How to use
### 1. Add the [Percentile_Norm-0.4.2-SNAPSHOT.jar](https://github.com/WeisongZhao/percentile_normalization.imagej/releases/download/v0.4.2/Percentile_Norm-0.4.2-SNAPSHOT.jar) to your imageJ plugin folder as usual and it will show up in `process->Percentile_Normalization`:
### 2. The mainboard and brief introduction to some confusing concepts in it
* ### the two number(35,657 in this snapshot) under the histogram:
The left one represent the smallest pixel value in the scope(a slice or stack depending on the *Mode* setting).Accordingly the right one stands for the biggest value in the scope.
* ### Mode:
this configuration is to specify the scope where we get the x% rank. So the stack scope and slice scope is pretty easy to understand now.
+ ### Set:
The same function as the sliders above.Just another way the get the input argument lower_percentile and the upper_percentile.
* ### Reset
Undo the changes that happens after the last apply.
* ### Auto
Just click this buttom, the appropriate manipulation may happens on your image.
* ### Apply
Only do the apply can we save it in the disk later.
## 3.Here comes a presentation.
