Physical Wavelet Frame Denoising
By Rongfeng Zhang, T. Ulrych. 2003
145 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Geophysics
View Paper: https://www.semanticscholar.org/paper/071f85a8a56b1e437fb9b6972265642fc59f5d8e
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
- Jianwei Ma
- Siwei Yu
- Xiaoqun Zhang
- Mauricio D. Sacchi
- Wenlong Wang
- Xiaohong Chen
- Mostafa Naghizadeh
- Guochang Liu
In Geophysics
- Siwei Yu
- Jianwei Ma
- Xiaoqun Zhang
- Wenlong Wang
- Mauricio D. Sacchi
- Xiaohong Chen
- Mostafa Naghizadeh
- Jingwei Liang
Citations in the corpus
- Deep learning for denoising (2018, background): “…assumed as Gaussian distributed while the data can satisfy different assumptions, such as linear events (Spitz, 1991; Naghizadeh, 2012), sparsity (Zhang and Ulrych, 2003; Hennenfent and Herrmann, 2006; Fomel and Liu, 2013; Yu et al., 2015), and low-rank assumptions (Trickett, 2008; Kreimer and…”
- Interpolation and denoising of high-dimensional seismic data by learning a tight frame (2015, background): “Physical wavelet (Zhang and Ulrych, 2003) is a specially designed wavelet for seismic data by considering hyperbolic features of seismic events.”