Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
A multiresolution framework for volume rendering
VVS '94 Proceedings of the 1994 symposium on Volume visualization
A Multiscale Model for Structure-Based Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Evaluation and Design of Filters Using a Taylor Series Expansion
IEEE Transactions on Visualization and Computer Graphics
Structure-Significant Representation of Structured Datasets
IEEE Transactions on Visualization and Computer Graphics
Physics-Based Feature Mining for Large Data Exploration
Computing in Science and Engineering
Volume Data and Wavelet Transforms
IEEE Computer Graphics and Applications
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Mathematical properties of the JPEG2000 wavelet filters
IEEE Transactions on Image Processing
CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
Improved edge preserving lossy image compression using wavelet transform
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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High-fidelity wavelet transforms can facilitate visualization and analysis of large scientific data sets. However, it is important that salient characteristics of the original features be preserved under the transformation. We present a set of filter design axioms in the spatial domain which ensure that certain feature characteristics are preserved from scale to scale and that the resulting filters correspond to wavelet transforms admitting in-place implementation. We demonstrate how the axioms can be used to design linear feature-preserving filters that are optimal in the sense that they are closest in L^2 to the ideal low pass filter. We are particularly interested in linear wavelet transforms for large data sets generated by computational fluid dynamics simulations. Our effort is different from classical filter design approaches which focus solely on performance in the frequency domain. Results are included that demonstrate the feature-preservation characteristics of our filters.