Multiresolution methods are deeply related to image processing, biological and computer
vision, scientific computing, etc. The curvelet transform is a multiscale directional transform …

This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …

We introduce a digital waveletlike transform, which is tailored specifically for representing
seismic data. The transform provides a multiscale orthogonal basis with basis functions …

Multiresolution methods are deeply related to image processing, biological and computer
vision, and scientific computing. The curvelet transform is a multiscale directional transform …

This thoroughly updated new edition presents state of the art sparse and multiscale image
and signal processing. It covers linear multiscale geometric transforms, such as wavelet …

We extend our earlier work on the nonequispaced fast discrete curvelet transform (NFDCT)
and introduce a second generation of the transform. This new generation differs from the …

Seismic imaging is a linearized inversion problem relying on the minimization of a least‐
squares misfit functional as a function of the medium perturbation. The success of this …

By estimating local event slopes in prestack seismic reflection data, it is possible to
accomplish all common time-domain imaging tasks, from normal moveout to prestack time …

Despite the fact that wavelets have had a wide impact in image processing, they fail to
efficiently represent objects with highly anisotropic elements such as lines or curvilinear …

Coherent seismic noise is usually difficult to attenuate due to the similar morphological
patterns between noise and useful signals. To attenuate coherent noise, special …