Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results.

Complex wavelet transforms offer the opportunity to perform directional and coherent processing based on the local magnitude and phase of signals and images. Although denoising, segmentation, and image enhancement are significantly improved using complex wavelets, the redundancy of most current transforms hinders their application in compression and related problems. In this paper we introduce a new orthonormal complex wavelet transform with no redundancy for both real- and complex-valued signals. The transform's filter bank features a real low pass filter and two complex high pass filters arranged in a critically sampled three-band structure. Placing symmetry and orthogonality constraints on these filters, we find that each high-pass filter can be factored into a real high pass filter followed by an approximate Hilbert transform filter.

Shift sensitivity, poor directional selectivity and lack of phase information are three major disadvantages of the discrete wavelet transform, In earlier research, we demonstrated that projection-based complex wavelet transforms have excellent directional selectivity and explicit phase information, In this paper, we discuss the theory of projection-based complex wavelet transforms and we prove that these transforms can have reduced shift sensitivity with low transform redundancy. We also provide experimental results to demonstrate the reduced shift sensitivity of our transforms.

Shift variance and poor directional selectivity, two major disadvantages of the discrete wavelet transform, have previously been circumvented either by using highly redundant, non-separable wavelet transforms or by using restrictive designs to obtain a pair of wavelet trees with a transform-domain redundancy of 4.0 in 2D. We demonstrate that excellent shift-invariance properties and directional selectivity may be obtained with a transform-domain redundancy of only 2.67 in 2D. We achieve this by projecting the wavelet coefficients from Selesnick's (Selesnick et al., 2000) almost-shift-invariant, double-density wavelet transform so as to separate approximately the positive and negative frequencies, thereby increasing directionality. Subsequent decimation and a novel inverse projection maintain the low redundancy while ensuring perfect reconstruction. Although our transform generates complex-valued coefficients allowing processing capabilities that are impossible with real-valued coefficients, it may be implemented with a fast algorithm that uses only real arithmetic. To demonstrate the efficacy of our new transform, we show that it achieves state-of-the-art performance in a seismic image-processing application.

Shift sensitivity and poor directionality, two major disadvantages of the discrete wavelet transform, have previously been circumvented either by using highly redundant, non-separable wavelet transforms or by using restrictive designs to obtain a pair of wavelet trees with a transform-domain redundancy of 4.0 in 2D. We demonstrate that excellent shift-invariance properties and directional selectivity may be obtained with a transform-domain redundancy of only 2.67 in 2D. We achieve this by projecting the wavelet coefficients from Selesnick's (see Wavelet Applications VII, Proceedings of SPIE, 2000) shift-insensitive, double-density wavelet transform so as to separate approximately the positive and negative frequencies, thereby increasing directionality. Subsequent decimation and a novel inverse projection maintain the low redundancy while ensuring perfect reconstruction. Although our transform generates complex-valued coefficients that provide valuable phase information, it may be implemented with a fast algorithm that uses only real arithmetic. To demonstrate the efficacy of our new transform, we show that it achieves state-of-the-art performance in a seismic image-processing application.

Poor directional selectivity, a major disadvantage of the 2D separable discrete wavelet transform (DWT), has heretofore been circumvented either by using highly redundant, nonseparable wavelet transforms or by using restrictive designs to obtain a pair of wavelet trees. In this paper, we demonstrate that superior directional selectivity may be obtained with no redundancy in any separable wavelet transform. We achieve this by projecting the wavelet transform coefficients onto the Softy space of signals and decimating before processing. A novel reconstruction step guarantees perfect reconstruction within this critically-sampled framework.