The Curvelet Transform
By Jianwei Ma, Gerlind Plonka-Hoch. IEEE Signal Processing Magazine, 2010
450 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/56324c0dd16bdffb8aef2b70c275d4371b7ac806
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Miguel López-Benítez
- Daniel Sage
- Helmut Bölcskei
- Zsuzsanna Püspöki
- Fei Ma
- Enggee Lim
- Thomas Wiatowski
- Xiaohong Chen
In Media Technology
- Daniel Sage
- Helmut Bölcskei
- Zsuzsanna Püspöki
- Thomas Wiatowski
- Xiaohong Chen
- Kevin W. Eliceiri
- Martin Storath
- Guochang Liu
Citations in the corpus
- Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer (2014, methodology): “Recent articles 43,44 have suggested that the CT method may be successfully applied in combination with other approaches for image processing such as fiber extraction, as we have demonstrated here.”
- Noncausal f–x–y regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data (2013, methodology): “Transform-base methods can also be used for seismic noise attenuation (Ma and Plonka, 2010). Tang and Ma (1991) proposed to total-variation-based curvelet shrinkage for 3D seismic data denoising in order to suppress nonsmooth artifacts caused by the curvelet transform.”
- Transforms and Operators for Directional Bioimage Analysis: A Survey. (2016, methodology): “1999) or constructions based on the Meyer window functions (Daubechies 1992; Ma and Plonka 2010).”
- A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities (2022, background): “Complex wavelet transform (complex WT) can overcome the above problems, but it has another problem [10].”
- A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction (2015, methodology): “Semi-discrete curvelet frames: Curvelets, introduced in [34], [38], are well-suited to the extraction of signal features characterized by curve-like singularities (such as, e.g., curved edges in images), and have been applied successfully in various practical feature extraction tasks [60], [61].”