ACT

Adaptive Curvelet Thresholding (ACT) for Removal of Additive Stationary White/Colored Gaussian Noise
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Aktualisiert 25. Mai 2022

Adaptive Curvelet Thresholding for Filtering of Additive Stationary White/Colored Gaussian Noise

This package contains the denoising filter for additive stationary white/colored(a.k.a. correlated) Gaussian noise based on the adaptive curvelet thresholding (ACT) method presented in:

N. Eslahi and A. Aghagolzadeh, "Compressive Sensing Image Restoration Using Adaptive Curvelet Thresholding and Nonlocal sparse Regularization", IEEE Trans. Image Process., vol.25, no.7, pp. 3126-3140, Jul. 2016 https://doi.org/10.1109/TIP.2016.2562563

Author: Nasser Eslahi (Tampere University, Finland)

Contents

The ACT package includes:

ACT_filter.m the denoising filter based on ACT
demo_denoising.m a demo code for denoising of additive stationary Gaussian noise
./DCuT/fdct_wrapping.m forward discrete curvelet transform
./DCuT/ifdct_wrapping.m inverse/backward discrete curvelet transform
./Test_Images some images for testing the denoising performance

For a detailed information on each function, please check its corresponding syntax, e.g.,

  doc ACT_filter

Visualization of some random tests

demo_denoising.mp4

Disclaimer

Copyright (c) 2006-2022 All rights reserved. This work should be used for nonprofit purposes and non-commercial use only, see LICENSE.

Feedback

If you have any comment, suggestion, or question, please do contact Nasser Eslahi
nasser.eslahi@tuni.fi
nasser.eslahi@gmail.com

Zitieren als

Nasser Eslahi (2024). ACT (https://github.com/NEslahi/ACT), GitHub. Abgerufen .

Kompatibilität der MATLAB-Version
Erstellt mit R2018a
Kompatibel mit allen Versionen
Plattform-Kompatibilität
Windows macOS Linux

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Versionen, die den GitHub-Standardzweig verwenden, können nicht heruntergeladen werden

Version Veröffentlicht Versionshinweise
1.0.0

Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.
Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.