Articles | Volume 27, issue 2
Nonlin. Processes Geophys., 27, 253–260, 2020
https://doi.org/10.5194/npg-27-253-2020
Nonlin. Processes Geophys., 27, 253–260, 2020
https://doi.org/10.5194/npg-27-253-2020

Research article 24 Apr 2020

Research article | 24 Apr 2020

Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details

Xiang-Yu Jia and Chang-Lei DongYe

Related subject area

Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Solid earth, continental surface, biogeochemistry | Techniques: Theory
Application of Lévy Processes in Modelling (Geodetic) Time Series With Mixed Spectra
Jean-Philippe Montillet, Xiaoxing He, Kegen Yu, and Changliang Xiong
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-23,https://doi.org/10.5194/npg-2020-23, 2020
Revised manuscript accepted for NPG
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Cited articles

Balovsyak, S. V. and Odaiska, K. S.: Automatic determination of the gaussian noise level on digital images by high-pass filtering for regions of interest, Cybern. Syst. Anal., 54, 662–670, https://doi.org/10.1007/s10559-018-0067-3, 2018. 
Bhutada, G. G., Anand, R. S., and Saxena, S. C.: Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform, Digit. Signal Process., 21, 118–130, https://doi.org/10.1016/j.dsp.2010.09.002, 2011. 
Cheng, H. D. and Shi, X. J.: A simple and effective histogram equalization approach to image enhancement, Digit. Signal Process., 14, 158–170, https://doi.org/10.1016/j.dsp.2003.07.002, 2004. 
Cho, H., Lee, H., Kang, H., and Lee, S.: Bilateral texture filtering, ACM T. Graphic., 33, 1–8, https://doi.org/10.1145/2601097.2601188, 2014. 
Farbman, Z., Fattal, R., Lischinski, D., and Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation, ACM T. Graphic., 27, 1–10, https://doi.org/10.1145/1360612.1360666, 2008. 
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Short summary
We proposed a texture detail enhancement method for seismic section image. Wavelet transform can effectively separate structure information and detail information of an image. High-frequency noise in structural information can be estimated and removed effectively by using bilateral texture filter in a low-frequency sub-band. In the high-frequency sub-band, adaptive enhancement transform can be used to enhance the image edge and texture information and effectively remove the low-frequency noise.