Foundations and Trends in Signal Processing
The Autonomous Underwater Vehicle Vision Denoising Method of Surfacelet Based on Sample Matrix
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Implementational aspects of the contourlet filter bank and application in image coding
EURASIP Journal on Advances in Signal Processing
A computable fourier condition generating alias-free sampling lattices
IEEE Transactions on Signal Processing
Representation and compression of multidimensional piecewise functions using surflets
IEEE Transactions on Information Theory
On hybrid directional transform-based intra-band image coding
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Uniform discrete curvelet transform
IEEE Transactions on Signal Processing
Progressive 3D model compression based on surfacelet
Edutainment'10 Proceedings of the Entertainment for education, and 5th international conference on E-learning and games
3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform
Journal of Mathematical Imaging and Vision
Video denoising based on adaptive shrinkage in surfacelet transform domain
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Multisensor video fusion based on spatial-temporal salience detection
Signal Processing
A multidimensional wave digital filter bank for video-based motion analysis
Multidimensional Systems and Signal Processing
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In 1992, Bamberger and Smith proposed the directional filter bank (DFB) for an efficient directional decomposition of 2-D signals. Due to the nonseparable nature of the system, extending the DFB to higher dimensions while still retaining its attractive features is a challenging and previously unsolved problem. We propose a new family of filter banks, named NDFB, that can achieve the directional decomposition of arbitrary N-dimensional (Nges2) signals with a simple and efficient tree-structured construction. In 3-D, the ideal passbands of the proposed NDFB are rectangular-based pyramids radiating out from the origin at different orientations and tiling the entire frequency space. The proposed NDFB achieves perfect reconstruction via an iterated filter bank with a redundancy factor of N in N-D. The angular resolution of the proposed NDFB can be iteratively refined by invoking more levels of decomposition through a simple expansion rule. By combining the NDFB with a new multiscale pyramid, we propose the surfacelet transform, which can be used to efficiently capture and represent surface-like singularities in multidimensional data