Simply denoise: Wavefield reconstruction via jittered undersampling

By Gilles Hennenfent, Felix J. Herrmann. 2008

323 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/309f0b4536e60fe28d31f43cc346ab9f7bba3dfb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

In Geophysics

Citations in the corpus

Nonequispaced curvelet transform for seismic data reconstruction: A sparsity-promoting approach (2010, methodology): “For more details on jittered sampling of seismic data, we refer to Hennenfent and Herrmann (2008) and Tang et al. (2009).”
Probing the Pareto Frontier for Basis Pursuit Solutions (2008, methodology): “In some applications (such as the seismic image reconstruction problem [32]) the size of this subset can become large, and thus the least-squares subproblems can become prohibitively expensive.”
Recovery of seismic wavefields based on compressive sensing by an l1-norm constrained trust region method and the piecewise random subsampling (2011, background): “, sine, cosine, wavelet and curvelet transforms (Herrmann et al., 2008).”
Compressed Synthetic Aperture Radar (2010, background): “Thus, before such methods that we will suggest are fielded, systematic studies will be needed that will depend on their intended use.”
Dreamlet-based interpolation using POCS method (2014, background): “A Jittered under-sampling algorithm is proposed (Hennenfent and Herrmann, 2008), which can keep random property as well as control the biggest gap between traces, avoiding the shortcomings of regular sampling and random sampling.”
Compressive sensing: From theory to applications, a survey (2013, methodology): “A CS based successful reconstruction theory is developed in this sense known as ‘Curvelet-based Recovery by Sparsitypromoting Inversion (CRSI)’ [69].”
Seismic data restoration via data-driven tight frame (2014, background): “, 2005; Abma and Kabir, 2006; Trad, 2009), seislet transform (Fomel and Liu, 2010; Liu and Fomel, 2010), and curvelet transform (Hennenfent and Herrmann, 2008; Herrmann and Hennenfent, 2008; Naghizadeh and Sacchi, 2010; Wang et al., 2011; Shahidi et al., 2013).”
Compressed Sensing (2014, unknown): “Estas abarcan diversas áreas como: imagenoloǵıa medica [49, 45], conversión analógico/digital [68], detección mediante RADAR [4, 42], corrección de errores [18], cámaras digitales [34, 21, 51], Sismoloǵıa [41], Radioastronomı́a [71], comunicación inalámbrica [63, 3, 65] y redes de sensores inalámbri”
ParNes: a rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals (2009, unknown): “This problem comes up in ﬁelds such as image processing [34], seismics [26, 25], astronomy [8], and model selection in regression [17].”