Multirate systems and filter banks
Multirate systems and filter banks
Inpainting and Zooming Using Sparse Representations
The Computer Journal
A new framework for complex wavelet transforms
IEEE Transactions on Signal Processing
The Shiftable Complex Directional Pyramid—Part I: Theoretical Aspects
IEEE Transactions on Signal Processing - Part I
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
Multidimensional Directional Filter Banks and Surfacelets
IEEE Transactions on Image Processing
Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion
IEEE Transactions on Image Processing
Contour extraction of gait recognition based on improved GVF Snake model
Computers and Electrical Engineering
Multimodality image fusion by using both phase and magnitude information
Pattern Recognition Letters
A flexible directional image representation using pseudo polar fourier transform based DFBs
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Multisensor video fusion based on spatial-temporal salience detection
Signal Processing
Statistical texture retrieval in noise using complex wavelets
Image Communication
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An implementation of the discrete curvelet transform is proposed in this work. The transform is based on and has the same order of complexity as the Fast Fourier Transform (FFT). The discrete curvelet functions are defined by a parameterized family of smooth windowed functions that satisfies two conditions: i) 2π periodic; ii) their squares form a partition of unity. The transform is named the uniform discrete curvelet transform (UDCT) because the centers of the curvelet functions at each resolution are positioned on a uniform lattice. The forward and inverse transform form a tight and self-dual frame, in the sense that they are the exact transpose of each other. Generalization to M dimensional version of the UDCT is also presented. The novel discrete transform has several advantages over existing transforms, such as lower redundancy ratio, hierarchical data structure and ease of implementation.