Seismic Signal Denoising Based on Region Segmentation Gradient Histogram Preservation

Liyuan Weng; Yatong Zhou; Jingfei He; Xiaolu Li

doi:10.3788/LOP57.101003

[1] Li H J. Research and application of broadband marine seismic exploration[D]. Changchun: Jilin University, 13-14(2016).

[2] Zhang B B, Zhang J H, Wu Y T. Research on protection and extension for seismic low frequencies[J]. Progress in Geophysics, 34, 1139-1144(2019).

[3] Chen Y K. Dip-separated structural filtering using seislet transform and adaptive empirical mode decomposition based dip filter[J]. Geophysical Journal International, 206, 457-469(2016).

[4] Huang Y, Wen X T, He Z H. Denoising algorithm of random noise with seismic image based on nonlocal means[J]. Fault-Block Oil and Gas Field, 20, 730-732(2013).

[5] Ren T T, Zhou Y T, Hao X X et al. Three-dimensional seismic signal denoising based on block matching and collaborative filtering[J]. Journal of Hebei University of Technology, 46, 1-7(2017).

[6] Xu C T, Cao J J[J]. The selection of weighted kernel function based on NLM algorithm Modern Computer, 2019, 68-70.

[7] Zhang Y, Ren W J, Tang G W. Random noise suppression on seismic data based on structured-clustering dictionary learning[J]. Oil Geophysical Prospecting, 53, 1119-1127(2018).

[8] Galatsanos N P, Katsaggelos A K. Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation[J]. IEEE Transactions on Image Processing, 1, 322-336(1992).

[9] Liu C H, Qi Y, Ding W R. SAR despeckling based on clustering dictionary learning and sparse representation[J]. Systems Engineering and Electronics, 39, 1709-1715(2017).

[10] Zhang Y S, Ji K F, Deng Z P et al. Clustering-based SAR image denoising by sparse representation with KSVD. [C]∥2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China. New York: IEEE, 5003-5006(2016).

[11] Jia F Q. Image denoising method based on gradient histogram preservation model[D]. Harbin: Harbin Institute of Technology, 31-34(2014).

[12] Zhao W, Tian Z, Yang L J et al. SAR image segmentation using local smoothing weighted graph cut[J]. Journal of Optoelectronics·Laser, 25, 2212-2218(2014).

[13] Pandey P, Richhariya V, Rajput V. Gradient histogram edge preservation with non-local mean filtering for image denoising. [C]∥2016 Online International Conference on Green Engineering and Technologies (IC-GET), November 19, 2016, Coimbatore, India. New York: IEEE, 16864592(2016).

[14] Song C W, Deng H, Gao H J et al. Bayesian non-parametric gradient histogram estimation for texture-enhanced image deblurring[J]. Neurocomputing, 197, 95-112(2016).

[15] Wang T H, Jia H Z, Shu H Z. Full-reference image quality assessment algorithm based gradient magnitude and histogram of oriented gradient[J]. Journal of Southeast University(Natural Science Edition), 48, 276-281(2018).

[16] Xiao X Y, Jing W B, Zhao H L. An improved image enhancement algorithm based on the peak-signal to noise ratio[J]. Journal of Changchun University of Science and Technology(Natural Science Edition), 40, 83-86, 92(2017).

[17] Deng J H, Wu P J, Yu H J et al. An image quality assessment method based on structure similarity of extended gradients[J]. Science Technology and Engineering, 18, 42-47(2018).

[18] Yu X C, Xu J D. A blind source separation method for mixed images with additive white Gaussian noise[J]. Journal of Beijing University of Posts and Telecommunications, 35, 120-123(2012).

[19] Feng N C. Sparse signal reconstruction theory and algorithm via prior information[D]. Chongqing: Southwest University, 9-15(2018).

[20] Li H W. A novel method for extracting object-of-interest from natural image by integrating prior knowledge[D]. Hefei: University of Science and Technology of China, 44-46(2009).

[21] Wang X L. Research of image model based on natural image's statistal priors and sparse priors[D]. Harbin: Harbin Institute of Technology, 23-25(2014).

[22] Chi S Q, Chen W C, Zhang L et al. Texture attribute analysis of 3D seismic signals based on strong background interference separation. C]∥2018 International Geophysical Conference and Exhibition. [S.l: s.n], 4(2018).

[23] Raad L, Galerne B. Efros and freeman image quilting algorithm for texture synthesis[J]. Image Processing on Line, 7, 1-22(2017).

[24] Qu G Z. The suppression of random noise and separation of ground roll in seismic signals based on sparse representation[D]. Hefei: Hefei University of Technology, 20-22(2016).

[25] Jiang X D, Zhang W, Wang Z X et al. Velocity calibration for downhole microseismic monitoring based on total variation(TV)regularization[J]. Computing Techniques for Geophysical and Geochemical Exploration, 40, 559-564(2018).

[26] Jia X N. Image deconvolution based on Fourier-total variation regularization[D]. Changchun: Jilin University, 1-5(2011).

[27] Dong W S, Li X, Zhang L et al. Sparsity-based image denoising via dictionary learning and structural clustering. [C]∥CVPR 2011, June 20-25, 2011, Colorado Springs, CO, USA. New York: IEEE, 457-464(2011).

[28] Dong W S, Zhang L, Shi G M et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 22, 1620-1630(2013).

[29] Zuo W M, Zhang L, Song C W et al. Gradient histogram estimation and preservation for texture enhanced image denoising[J]. IEEE Transactions on Image Processing, 23, 2459-2472(2014).

[30] Liao J S, Wang L G. Hyperspectral image classification method based on fusion with two kinds of spatial information[J]. Laser & Optoelectronics Progress, 54, 081002(2017).