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Image denoising - Golgi apparatus

Updated: Dec 6, 2022

This model removes image noise and enhances image signal for volumetric fluorescence microscopy images. This model is trained using the 3D-RCAN architecture, developed in-house (read about it on BioRxiv [1]).

The model is trained on 3D volumetric images of Golgi apparatus captured at 60x on an instant structured illumination microscopy (iSIM) system.


By downloading, installing, copying, accessing, or using the software, you agree to the terms of this end user license agreement.


Make sure you have Python versions 3.5 to 3.7 installed with TensorFlow-GPU version 1.13.0 and above.

Installation and apply instructions

Aivia is required for applying the model file. You can request a demo copy of Aivia here.

  1. Drag-and-drop the model file into the Recipe Console area; or use the 'Load recipe' option in the Recipe Console to load the model file.

  2. Load the test image (or any image of your own) into Aivia.

  3. If your image contains more than one channel, click on the 'Input & Output' section and specify the image channel you wish to apply the model on.

  4. Click 'Start' to apply the model.


  1. J Chen, et. al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. BioRxiv.


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