Spot noise texture synthesis for data visualization
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Imaging vector fields using line integral convolution
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
A posteriori error estimation and adaptive mesh-refinement techniques
ICCAM'92 Proceedings of the fifth international conference on Computational and applied mathematics
Solution of nonlinear diffusion appearing in image smoothing and edge detection
Applied Numerical Mathematics
Image quantization using reaction-diffusion equations
SIAM Journal on Applied Mathematics
UFLIC: a line integral convolution algorithm for visualizing unsteady flows
VIS '97 Proceedings of the 8th conference on Visualization '97
A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction
Journal of Mathematical Imaging and Vision
Recent numerical methods—a challenge for efficient visualization
Future Generation Computer Systems - Special issue on scientific visualization
Adaptive Projection Operators in Multiresolution Scientific Visualization
IEEE Transactions on Visualization and Computer Graphics
Multigrid anisotropic diffusion
IEEE Transactions on Image Processing
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Diffusion-Like reconstruction schemes from linear data models
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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Nonlinear diffusion methods have proved to be powerful methods in the processing of 2D and 3D images. They allow a denoising and smoothing of image intensities while retaining and enhancing edges. On the other hand, compression is an important topic in image processing as well. Here a method is presented which combines the two aspects in an efficient way. It is based on a semi-implicit Finite Element implementation of nonlinear diffusion. Error indicators guide a successive coarsening process. This leads to locally coarse grids in areas of resulting smooth image intensity, while enhanced edges are still resolved on fine grid levels. Special emphasis has been put on algorithmical aspects such as storage requirements and efficiency. Furthermore, a new nonlinear anisotropic diffusion method for vector field visualization is presented.