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Dr.-Ing. Chris Schwemmer

Alumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg


On this page, you can find implementations of various published algorithms that were useful in the course of my work, mostly for evaluation purposes.

All software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.

We took care in testing the implementations, but no guarantee for correctness is given.

Image Noise Estimation

An implementation of the image noise estimation algorithm published in

S.-M. Yang and S.-C. Tai: "Fast and reliable image-noise estimation using a hybrid approach". Journal of Electronic Imaging 19(3), pp. 033007-1–15, 2010.

Both a Initiates file downloadC++ and a Initiates file downloadMATLAB implementation are provided. The results differ slightly due to different convolution implementations. Both versions have no additional dependencies.

See also the Opens internal link in current windowImage Denoising Algorithms Archive on our website.

Fast normalized correlation metric for ITK 3.20

This is a small update to the Opens external link in new windowitkNormalizedCorrelationImageToImageMetric of Opens external link in new windowITK 3.20. It includes the B-spline weight caching that is part of the Opens external link in new windowitkMattesMutualInformationImageToImageMetric. If a B-spline is used as the registration transform, the caching is enabled automatically, so this class can be used as a direct replacement by simply changing the class name without further source code modifications.

Click here to Initiates file downloaddownload.