1887

Abstract

Summary

Block Matching Local SVD Operator Based Sparsity and TV Regularization (BMLSVDTV) method is a novel and effective denoising scheme in the field of image processing, which integrates group sparsity and global TV regularization in a unified framework and works well in attenuating random noise in image. The group sparsity is able to recover structures with more details clearly by exploiting the repetitiveness of the valid structures globally in images, and the global TV can suppress pseudo-Gibbs artifacts in the restoration. Because the reflection seismic data generally has a degree of redundancy due to the repetition of geological structures which satisfies the application conditions of this method, we propose to adopt the BMLSVDTV method to suppress the seismic random noise. Tests on synthetic and real seismic data demonstrate that the proposed method can suppress random noise much more effectively than the classical f-x deconvolution method and the Curvelet transform based method, and it preserves the seismic structures well without inducing pseudo-Gibbs artifacts.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901349
2019-06-03
2024-03-29
Loading full text...

Full text loading...

References

  1. Canales, L.L.
    , [1984] Random noise reduction. Seg Technical Program Expanded Abstracts3, 329–329.
    [Google Scholar]
  2. Candès, E., Demanet, L., Donoho, D., Ying, L.
    , [2006] Fast Discrete Curvelet Transforms. Multiscale Modeling & Simulation5, 861--899.
    [Google Scholar]
  3. Goldstein, T., Osher, S.
    , [2009] The Split Bregman Method for L1-Regularized Problems. SIAM Journal on Imaging Sciences2, 323–343.
    [Google Scholar]
  4. Ji, H., Liu, C., Shen, Z., Xu, Y.
    , [2010] Robust video denoising using Low rank matrix completion, Computer Vision & Pattern Recognition.
    [Google Scholar]
  5. Liu, J., Osher, S.
    , [2018]. Block Matching Local SVD Operator Based Sparsity and TV Regularization for Image Denoising. Journal of Scientific Computing.
    [Google Scholar]
  6. Ma, J., Plonka-Hoch, G.
    , [2007]. Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion. IEEE Transactions on Image Processing16, 2198–2206.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901349
Loading
/content/papers/10.3997/2214-4609.201901349
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error