Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images
International Journal of Computer Vision - Special issue on computer vision research at the Technion
Diffusions and Confusions in Signal and Image Processing
Journal of Mathematical Imaging and Vision
Digital Color Imaging Handbook
Digital Color Imaging Handbook
Image Statistics and Anisotropic Diffusion
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
On the Relation between Anisotropic Diffusion and Iterated Adaptive Filtering
Proceedings of the 30th DAGM symposium on Pattern Recognition
Patch Contour Matching by Correlating Fourier Descriptors
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
A general framework for low level vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Color image enhancement via chromaticity diffusion
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Autocorrelation-Driven Diffusion Filtering
IEEE Transactions on Image Processing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Application of backward stochastic differential equations to reconstruction of vector-valued images
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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Techniques from the theory of partial differential equations are often used to design filter methods that are locally adapted to the image structure. These techniques are usually used in the investigation of gray-value images. The extension to color images is non-trivial, where the choice of an appropriate color space is crucial. The RGB color space is often used although it is known that the space of human color perception is best described in terms of non-euclidean geometry, which is fundamentally different from the structure of the RGB space. Instead of the standard RGB space, we use a simple color transformation based on the theory of finite groups. It is shown that this transformation reduces the color artifacts originating from the diffusion processes on RGB images. The developed algorithm is evaluated on a set of real-world images, and it is shown that our approach exhibits fewer color artifacts compared to state-of-the-art techniques. Also, our approach preserves details in the image for a larger number of iterations.