Package: aydin
Aydinis a user-friendly, feature-rich, and fastimage denoising toolthat provides
a number ofself-supervised, auto-tuned, and unsupervisedimage denoising algorithms.Aydinhandles 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 withAydin Studioagraphical user interfaceto easily experiment with all the different algorithms and parameters available,
acommand line interfaceto run large jobs on the terminal possibly on powerfull remote machines,
and anAPIfor custom coding and integration into your scripts and applications.
More details and exhaustive explanations can be found in Aydin'sdocumentation.And, of course, a simplifiednapariplugin is in the works!
Notebooks for running on Collab are also planned.
The project is commercial-use-friendly if you specify pyside as your GUI backend.Supported algorithms:CurrentlyAydinsupports two main families of denoisers: the first family consists
of 'classical' denoising algorithms that leverage among other: frequency domain filtering,
smoothness priors, low-rank representations, self-similarity, and more.
The second family consists of algorithms that leverage machine learning approaches
such as convolutional neural networks (CNN) or gradient boosting (GB).
In theNoise2Self paperwe show that it is possible to calibrate any parameterised denoising algorithm,
from the few parameters of a classical algorithm to the millions of weights of a deep neural
network. We leverage and extend these ideas inAydinto provide a variety of auto-tuned
and trained high-quality image denoisers. What this means is that for example, we can discover automatically
the optimal parameters for non-local-means (NLM) denoising, or the best cut-off frequencies for a low-pass denoiser.
These parameters are difficult to determine 'by-hand' but when auto-tuned we show (seeuse-cases)
that you can get remarkable results even with simple 'classic' denoisers, and even be competitive against more complex and slower
approaches such as deep-learning based denoisers that can also be prone to hallucination and 'copy-paste' effects.
Importantly, our experience denoising many different kinds of images has shown that tehre is not a single 'silver-bullet'
denoiser, different kinds of datasets require different approaches.Here is the list of currently available methods:Low-pass filtering based algorithms:Butterworth denoiser (butterworth).Gaussian blur denoiser (gaussian).Gaussian-Median mixed denoiser (gm).Optimisation-based with smoothness priors:Total-Variation denoising (tv)Harmonic prior (harmonic)Low-rank representations:Denoising via sparse decomposition (e.g. OMP) over a fixed dictionary (DCT, DST, ...)Denoising via sparse decomposition (e.g. OMP) over a learned dictionary (Kmeans, PCA, ICA, SDL, ...)Patch similarity:Non-Local Means denoising (nlm)BMnD (not available just yet but partly implemented!)Machine learning based:Noise2Self-FGR: Noise2Self denoising via Feature Generation and Regression (FGR). We use specially crafted integral features. We have several variants that leverage different regressors: CatBoost(cb), lightGBM, linear, perceptron, random-forrest, support vector regression)Noise2Self-CNN: Noise2Self denoising via Convolutional Neural Networks (CNN). This is the original approach of Noise2Self. In our experience this is typically slower to train, and more prone to hallucination and residual noise than FGR.Other:Lipschitz continuity denoising.Some methods actually do combine multiple ideas and so the classification above is not strict.
We recommend trying first a good baseline denoiser such as theButterworth denoiser.
If unsatisfied with the result, and you have a powerful computer with a recent NVIDIA
graphics card, then we recommend you try the Noise2Self-FGR-cb denoiser.
For detailed use-cases checkhere.We regularly come up with new approaches and ideas, there is just not enough time to write papers about all these ideas.
This means that the best 'publication' for some of these novel algorithms is this repo itself, and so please be so kind as to
cite this repo (see below) for any ideas that you would use or reuse.
We have a long todo list of existing, modified, as well as original algorithms that we plan to add toAydinin the next weeks and months. We will do so progressively as time allows. Stay tuned!DocumentationAydin's documentation can be foundhere.Installation ofAydin StudioWe recommend that users that are not familiar with python start with our user-friendly UI.
Download it for your operating system here:The latest releases and standalone executables can be foundhereand detailed installation instructions ofAydin Studiofor all three operating systems can be foundhere.Installation ofAydinin Conda:First download and installminicondaoranaconda.Then create a conda environment to host aydin:condacreate-naydin_envpython=3.9Activate the environment:condaactivateaydin_envInstallAydinin the environment:pipinstallaydinCUDA dependencies:If you have a NVIDIA CUDA Graphics card, you should install the CUDA toolkit:condainstallcudatoolkitMac specific dependencies:For Macs (OSX) you first need to do:brewinstalllibompYou can installBrewby following the instructionshere.Ubuntu/Linux specific dependencies:If you encounter problems running Aydin in Ubuntu/linux,
please install the following package:sudoaptinstalllibqt5x11extras5How to run ?Assuming that you have installedAydinin an environment, you can:StartAydin Studiofrom the command line with:aydinRun the Command Line Interface (CLI) for denoising:aydindenoisepath/to/noisyimageGet help on command line usage:aydin-hRecommended Operating System VersionsLinux: Ubuntu 18.04+Windows: Windows 10OSX: Mojave 10.14.6+Recommended Hardware:Recommended specifications are: at least 16 Gb of RAM, ideally 32 Gb, and more for very large
images, a CPU with at least 4 cores, preferably 16 or more, and a recent NVIDIA graphics card such as a RTX series card.
Older graphics cards could work but may cause trouble or be too slow.AydinStudio's summary page
gives an overview of the strengths and weaknesses of your machine, highlighting in red and orange
items that might be problematic.Known Issues:Here are some issues that are being actively addressed and will be addressed asap:Stop button for all algorithms. For technical reasons having to do with the diversity of libraries we use, we currently cannot stop training. We are planning to solve this using subprocess spawning. For now, to stopAydinfrom running, you need to close the window and/or terminate the process. We know this is very unfortunate and are determined to fix this quickly.On Ubuntu and perhaps other Linux systems, high-dpi modes tend to mess with font and ui element rendering.M1 Macs are not yet supported, we depend on libraries that have not yet made the move, yet! Hopefully we will soon be able to run Aydin on M1 Macs!Road Map:Planned features:Toggling between 'Advanced' and 'Basic' modes to show and hide advanced algorithms.:white_check_mark:Loading of denoising model and configurations (JSON) onAydin StudioPytorch backendContributingFeel free to check ourcontributing guidelinefirst and start
discussing your new ideas and feedback with us through issues.