This is the github location for image denoising ImageJ tool and its source codee #Author: Varun Mannam #Contributors: Yide Zhang #Details: The Department of Electrical Engineering, The University of Notre Dame, South Bend, Indiana (IN), USA. Zip: 46556 #email: [email protected] paper: Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks https://www.osapublishing.org/abstract.cfm?uri=Microscopy-2020-MW2A.3
#Images: The test images can be downloaded from here https://curate.nd.edu/show/f4752f78z6t
#Citation for dataset: Please cite the FMD dataset using the following format: Mannam, Varun, Yide Zhang, and Scott Howard. “Fluorescence Microscopy Denoising (FMD) Dataset.” Notre Dame, April 21, 2019. https://doi.org/10.7274/r0-ed2r-4052. #DOI: 10.7274/r0-ed2r-4052
Description: This is the plugin used to denoise any fluorescence microscopy image that contains combination of Poisson-Gaussian noise. This algorithm is developed by training the noisy microscopic images with the convolutional neural networks using the U-Net architecture.
Steps to get a denoised image: 1a. Open Fiji/ImageJ 1b. ImageJ -> Edit -> Options -> Tensorflow -> Choose the Tensorflow TF version based on the user system requirements (like: CPU or GPU with proper CUDA drivers) 2. Select an image in ImageJ (use open image function: File ->open) 3. Run the image-denoising plugin (Plugins -> Image denoising -> U-Net denosing) then denoised image will pop-up. (by default denoised image is 32-bit float type and use ImageJ to combine the color images or other functions) 4. Use the console to check for the test time.
Limitations:
- Plugin is limiteed in the number of images at a time to denoise (limitation on the TF memory)
- If the image size is not multiple of 32x32, linear interpolation is used before passing through the model and again resized to the original image dimension.
- Speed is better in presence of GPU machine compared to the CPU version.
- 4D images support is not added yet this stage.
- GPU common errors are linking the CUDA drivers using symbolic names.
© 2019 Varun Mannam, University of Notre Dame
Licensed under the GPL