1. Greycstoration Mac
::Latest News

GREYCstoration is an image regularization algorithm which is able to process a color image by locally removing small variations of pixel intensities while preserving significant global image features, such as edges and corners. The most direct application of image regularization is image denoising. GREYCstoration will allow you to process a color image by locally removing small variations of pixel intensities GREYCstoration is an image regularization algorithm that will allow you to process a color image by locally removing small variations of pixel intensities while preserving significant global image features, such as edges and corners.


Mac

Use G'MIC instead of GREYCstoration!
GREYCstoration is not maintained anymore : We have developed a new plug-in for GIMP, called G'MIC (GREYC's Magic for Image Computing). This is a complete image processing toolbox which contains all the GREYCstoration features (of course), but also much much more filters for image denoising, enhancement, applying artistic effects and so on... Basically, if you appreciate using GREYCstoration, you will love G'MIC :) Here is a screenshot of G'MIC in action.

GREYCstorationgimppcwin32.exe: the GIMP plug-in for Windows-users. Simply copy the file to the plugins-folder in the user-space. The plugin works fine also on 2.3.×. If you’re using a self-compiled GIMP on a 64-bits architecture do the following: change the directory to /src/ and enter “make gimp”. The generated file then copy to.

::What is it ?

GREYCstoration is an image regularization algorithm which is able to process a color image by locally removing small variations of pixel intensities while preserving significant global image features, such as edges and corners. The most direct application of image regularization is image denoising. By extension, it can also be used to inpaint or resize images.
GREYCstoration is based on state-of-the-art image processing methods using nonlinear multi-valued diffusion PDE's (Partial Differential Equations). This kind of method generally outperforms basic image filtering techniques (such as convolution, median filtering, etc.), classically encountered in image painting programs. Other comparable image denoising techniques are available (for instance, Noise Ninja, Neat Image ) but are not open-source, and the corresponding algorithms are kept secret. On the contrary, the source code of GREYCstoration is freely available and distributed under the CeCILL License (compatible with the well-known GPL). It gives similar results (not to say better) to existing closed-source denoising filters, and is absolutely free to use.


::Author

David Tschumperlé,
(CNRS researcher in the Image Team of the GREYC Lab (UMR CNRS 6072) in Caen/France),
with the help of various contributors including David Cortesi, Nikita Melnichenko, Grzegorz Szwoch, Michel Talon.


::Related publications

The GREYCstoration algorithm is a free implementation of the different regularization PDE methods which were published in :
[1] D. Tschumperlé, L. Brun. Defining Some Variational Methods on the Space of Patches : Application to Multi-Valued Image Denoising and Registration, Research Report 'Les Cahiers du GREYC', No.08-01, March 2008.
[2] D. Tschumperlé. Fast Anisotropic Smoothing of Multi-Valued Images using Curvature-Preserving PDE's, International Journal of Computer Vision, IJCV(68), No 1, June 2006, pp.65-82.
Other related articles can be found in :
[3] D. Tschumperlé. Curvature-Preserving Regularization of Multi-Valued Images using PDE's, European Conference on Computer Vision (ECCV'2006).
[4] D. Tschumperlé. LIC-Based Regularization of Multi-Valued Images, IEEE International Conference on Image Processing (ICIP), September 2005.
The GREYCstoration algorithm is the logical sequel of my PhD work done in the Odyssee Team at the INRIA Sophia-Antipolis, under the supervision of Rachid Deriche. I had the great pleasure to write some papers about vector-valued regularization PDE's with him, more precisely :
[5] D. Tschumperlé, R. Deriche. Vector-Valued Image Regularization with PDE's : A Common Framework for Different Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), April 2005,
[6] D. Tschumperlé, R. Deriche. Vector-Valued Image Regularization with PDE's : A Common Framework for Different Applications, IEEE Conference on Computer Vision and Pattern Recognition, 2003,

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