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. Compared to other PDE-based regularization methods, our approach has several advantages : It performs very fast and is able to preserve thin image details since it works at a sub-pixel accuracy.
The tool is still a little bit hard to use (command-line based), but I hope the simple C++ API will ease the integration of the algorithm in more user-friendly interfaces. Previous versions of GREYCstoration are already available in Digikam and Krita.
There is a GIMP plug-in version available now which makes it a lot easier, I hope to see a Photoshop version soon.
There are a couple of GUI version here and here too.
You can download it here:
Important note : You will need to install the ImageMagick’s package in order to be able to read compressed image formats (JPG,PNG,etc…). On Unix systems, this package is often installed by default, on Windows, you can get it here. Please download one of the static package only.
You can read more here.