.. image:: resources/aydin_logo_grad_black.png :width: 400 :alt: Logo text *Image denoising, but chill...* Aydin is a user-friendly, feature-rich, and fast image denoising tool that provides a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms. Aydin handles from the get-go n-dimensional array-structured images with an arbitrary number of batch dimensions, channel dimensions, and typically up to 4 spatio-temporal dimensions. It comes with `Aydin Studio` a `graphical user interface `_ to easily experiment with all the different algorithms and parameters available, a `command line interface `_ to run large jobs offline, and an `API `_ for custom coding and integration into other packages. Our source code repository can be found `on GitHub `_. You can `open issues `_ to communicate bug reports and feature requests and find `our releases `_. Getting started, with bundles: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To get started with Aydin, please download and install Aydin on your machine. .. raw:: html


Getting started, with pip: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Aydin is support only on Python 3.9 currently. You can install Aydin with the following line: .. code-block:: bash pip install aydin .. image:: https://static.pepy.tech/personalized-badge/aydin?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads :target: https://pepy.tech/project/aydin Requirements ~~~~~~~~~~~~~ While Aydin works even on tiny laptops, it will run faster and better if you have a Nvidia graphics card. We also recommend at least 16GB of RAM but more is better especially for very large gigabyte-sized images. In the absence of a GPU, the more CPU cores the better, obviously. Different algorithms have different performance profiles, some of our best algorithms can easily run on modest machines, but you will need patience. Documentation ~~~~~~~~~~~~~~ Tutorials can be found `here `_ for an exhaustive tour of features and parameters for both the graphical interface (Aydin Studio), command line interface (CLI), and Python programming interface (API). To better understand how to tune parameters for a given image, please check our `use cases `_ where we go through. How to cite? ~~~~~~~~~~~~~ If you find Aydin useful and use it in your work, please kindly consider to cite us: .. image:: https://zenodo.org/badge/188953977.svg :target: https://zenodo.org/badge/latestdoi/188953977 .. toctree:: :maxdepth: 1 :hidden: :caption: Getting Started Install Hardware Requirements .. toctree:: :maxdepth: 1 :hidden: :caption: Use Cases Introduction Denoising Basics with Aydin Noisy ‘New York’ Test Image Spinning-Disk Confocal Images of Zebrafish Embryos Spinning-Disk Confocal Microscopy Images of Mouse Embryos OpenCell Images Chicken Embryos LSM 780 Images .. toctree:: :maxdepth: 1 :hidden: :caption: Tutorials Tutorials Home Aydin Studio (GUI) Tutorials Aydin CLI Tutorials Aydin API Tutorials .. toctree:: :maxdepth: 2 :hidden: :caption: API Reference Introduction Restoration Image Translator Transforms Feature Generator Regressors IO NN Options JSON .. toctree:: :maxdepth: 1 :hidden: :caption: Contact Us On Github On image.sc