Denoising with weak signal preservation by group-sparsity transform learning

[1]  Jian-Feng Cai,et al.  Data-driven tight frame construction and image denoising , 2014 .

[2]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Felix J. Herrmann,et al.  Non-parametric seismic data recovery with curvelet frames , 2008 .

[4]  Yoram Bresler,et al.  $\ell_{0}$ Sparsifying Transform Learning With Efficient Optimal Updates and Convergence Guarantees , 2015, IEEE Transactions on Signal Processing.

[5]  Necati Gulunay,et al.  FXDECON and complex wiener prediction filter , 1986 .

[6]  Haibin Di,et al.  3D structural-orientation vector guided autotracking for weak seismic reflections: A new tool for shale reservoir visualization and interpretation , 2018, Interpretation.

[7]  M. Baan,et al.  Local singular value decomposition for signal enhancement of seismic data , 2007 .

[8]  Yu Zhang,et al.  Antileakage Fourier transform for seismic data regularization , 2005 .

[9]  Lina Liu,et al.  Sparse graph-regularized dictionary learning for suppressing random seismic noiseGraph dictionary learning , 2018 .

[10]  Yanjun Li,et al.  Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  Mirko van der Baan,et al.  Random and coherent noise attenuation by empirical mode decomposition , 2009 .

[12]  Michael Elad,et al.  Analysis versus synthesis in signal priors , 2006, 2006 14th European Signal Processing Conference.

[13]  Stephen K. Chiu,et al.  Coherent and random noise attenuation via multichannel singular spectrum analysis in the randomized domain , 2013 .

[14]  Sanyi Yuan,et al.  Inversion-Based 3-D Seismic Denoising for Exploring Spatial Edges and Spatio-Temporal Signal Redundancy , 2018, IEEE Geoscience and Remote Sensing Letters.

[15]  Yanjun Li,et al.  When sparsity meets low-rankness: Transform learning with non-local low-rank constraint for image restoration , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  G. Easley,et al.  Sparse directional image representations using the discrete shearlet transform , 2008 .

[17]  Mauricio D. Sacchi,et al.  Interpolation and denoising of high-dimensional seismic data by learning a tight frame , 2015 .

[18]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[19]  Gerlind Plonka-Hoch,et al.  The Curvelet Transform , 2010, IEEE Signal Processing Magazine.

[20]  Evan Bianco,et al.  Geophysical tutorial: Well-tie calculus , 2014 .

[21]  Mauricio D. Sacchi,et al.  FX Singular Spectrum Analysis , 2009 .

[22]  P. E. Harris,et al.  Improving the performance of f-x prediction filtering at low signal-to-noise ratios , 1997 .

[23]  James H. McClellan,et al.  Iterative interferometry-based method for picking microseismic events , 2017, 1703.03154.

[24]  Yoram Bresler,et al.  Online Sparsifying Transform Learning—Part II: Convergence Analysis , 2015, IEEE Journal of Selected Topics in Signal Processing.

[25]  Yoram Bresler,et al.  Online Sparsifying Transform Learning— Part I: Algorithms , 2015, IEEE Journal of Selected Topics in Signal Processing.

[26]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[27]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..

[28]  Necati Gulunay,et al.  Seismic trace interpolation in the Fourier transform domain , 2003 .

[29]  Vivek K. Goyal,et al.  Transform-domain sparsity regularization for inverse problems in geosciences , 2009, GEOPHYSICS.

[30]  Lijun Zhu,et al.  Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering , 2016, ArXiv.

[31]  Guillermo Sapiro,et al.  Non-local sparse models for image restoration , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[32]  Jianwei Ma,et al.  Simultaneous dictionary learning and denoising for seismic data , 2014 .

[33]  Hui Zhou,et al.  Dictionary learning based on dip patch selection training for random noise attenuation , 2019, GEOPHYSICS.

[34]  Jianwei Ma,et al.  Seismic data restoration via data-driven tight frame , 2014 .

[35]  Yoram Bresler,et al.  Learning Sparsifying Transforms , 2013, IEEE Transactions on Signal Processing.

[36]  Yangkang Chen,et al.  Random noise attenuation using local signal-and-noise orthogonalization , 2015 .

[37]  Entao Liu,et al.  Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient , 2019, IEEE Access.

[38]  Paul Sava,et al.  Madagascar: open-source software project for multidimensional data analysis and reproducible computational experiments , 2013 .

[39]  Yoram Bresler,et al.  VIDOSAT: High-Dimensional Sparsifying Transform Learning for Online Video Denoising , 2017, IEEE Transactions on Image Processing.

[40]  David Zhang,et al.  Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[41]  Stanley Osher,et al.  Monte Carlo data-driven tight frame for seismic data recovery , 2016 .

[42]  Mauricio D. Sacchi,et al.  Interpolation and extrapolation using a high-resolution discrete Fourier transform , 1998, IEEE Trans. Signal Process..

[43]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[44]  L. Canales.  Random Noise Reduction , 1984 .

[45]  Yoram Bresler,et al.  Structured Overcomplete Sparsifying Transform Learning with Convergence Guarantees and Applications , 2015, International Journal of Computer Vision.

[46]  Sergey Fomel,et al.  OC-seislet: Seislet transform construction with differential offset continuation , 2010 .

[47]  Wen Gao,et al.  Group-Based Sparse Representation for Image Restoration , 2014, IEEE Transactions on Image Processing.