激光与光电子学进展, 2020, 57 (10): 101003, 网络出版: 2020-05-08  

基于区域分割梯度直方图保持的地震信号去噪 下载: 862次

Seismic Signal Denoising Based on Region Segmentation Gradient Histogram Preservation
作者单位
河北工业大学电子信息工程学院, 天津 300401
引用该论文

翁丽源, 周亚同, 何静飞, 李晓璐. 基于区域分割梯度直方图保持的地震信号去噪[J]. 激光与光电子学进展, 2020, 57(10): 101003.

Liyuan Weng, Yatong Zhou, Jingfei He, Xiaolu Li. Seismic Signal Denoising Based on Region Segmentation Gradient Histogram Preservation[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101003.

参考文献

[1] 李洪建. 海洋宽频地震勘探方法与应用研究[D]. 长春: 吉林大学, 2016: 13- 14.

    Li HJ. Research and application of broadband marine seismic exploration[D]. Changchun: Jilin University, 2016: 13- 14.

[2] 张彬彬, 张军华, 吴永亭. 地震数据低频信号保护与拓频方法研究[J]. 地球物理学进展, 2019, 34(3): 1139-1144.

    Zhang B B, Zhang J H, Wu Y T. Research on protection and extension for seismic low frequencies[J]. Progress in Geophysics, 2019, 34(3): 1139-1144.

[3] Chen Y K. Dip-separated structural filtering using seislet transform and adaptive empirical mode decomposition based dip filter[J]. Geophysical Journal International, 2016, 206(1): 457-469.

[4] 黄英, 文晓涛, 贺振华. 地震图像随机噪声的非局部均值去噪法[J]. 断块油气田, 2013, 20(6): 730-732.

    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, 2013, 20(6): 730-732.

[5] 任婷婷, 周亚同, 郝茜茜, 等. 基于块匹配协同滤波的三维地震信号去噪[J]. 河北工业大学学报, 2017, 46(4): 1-7.

    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, 2017, 46(4): 1-7.

[6] 徐翠婷, 曹剑剑. 基于NLM算法的加权核函数选取研究[J]. 现代计算机, 2019( 10): 68- 70.

    Xu CT, Cao JJ. The selection of weighted kernel function based on NLM algorithm[J]. Modern Computer, 2019( 10): 68- 70.

[7] 张岩, 任伟建, 唐国维. 应用结构聚类字典学习压制地震数据随机噪声[J]. 石油地球物理勘探, 2018, 53(6): 1119-1127.

    Zhang Y, Ren W J, Tang G W. Random noise suppression on seismic data based on structured-clustering dictionary learning[J]. Oil Geophysical Prospecting, 2018, 53(6): 1119-1127.

[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, 1992, 1(3): 322-336.

[9] 刘春辉, 齐越, 丁文锐. 基于聚类字典学习和稀疏表示的SAR图像抑斑方法[J]. 系统工程与电子技术, 2017, 39(8): 1709-1715.

    Liu C H, Qi Y, Ding W R. SAR despeckling based on clustering dictionary learning and sparse representation[J]. Systems Engineering and Electronics, 2017, 39(8): 1709-1715.

[10] Zhang YS, Ji KF, Deng ZP, 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, 2016: 5003- 5006.

[11] 贾福青. 基于梯度直方图保持模型的图像去噪方法[D]. 哈尔滨: 哈尔滨工业大学, 2014: 31- 34.

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

[12] 赵伟, 田铮, 杨丽娟, 等. 基于局部平滑加权图割方法的SAR图像分割[J]. 光电子·激光, 2014, 25(11): 2212-2218.

    Zhao W, Tian Z, Yang L J, et al. SAR image segmentation using local smoothing weighted graph cut[J]. Journal of Optoelectronics·Laser, 2014, 25(11): 2212-2218.

[13] PandeyP, RichhariyaV, RajputV. 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, 2016: 16864592.

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

[15] 王同罕, 贾惠珍, 舒华忠. 基于梯度幅度和梯度方向直方图的参考图像质量评价算法[J]. 东南大学学报(自然科学版), 2018, 48(2): 276-281.

    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), 2018, 48(2): 276-281.

[16] 肖祥元, 景文博, 赵海丽. 基于峰值信噪比改进的图像增强算法[J]. 长春理工大学学报(自然科学版), 2017, 40(4): 83-86, 92.

    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), 2017, 40(4): 83-86, 92.

[17] 邓杰航, 毋鹏杰, 余汉君, 等. 基于扩展梯度算子的结构相似度图像质量评价方法[J]. 科学技术与工程, 2018, 18(27): 42-47.

    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, 2018, 18(27): 42-47.

[18] 余先川, 徐金东. 一种抗加性高斯白噪声的盲图像源分离方法[J]. 北京邮电大学学报, 2012, 35(4): 120-123.

    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, 2012, 35(4): 120-123.

[19] 冯念慈. 基于先验信息的稀疏信号重构理论与算法研究[D]. 重庆: 西南大学, 2018: 9- 15.

    Feng NC. Sparse signal reconstruction theory and algorithm via prior information[D]. Chongqing: Southwest University, 2018: 9- 15.

[20] 李宏伟. 一种综合先验信息的从自然图像中提取感兴趣物体的新方法[D]. 合肥: 中国科学技术大学, 2009: 44- 46.

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

[21] 王旭林. 基于自然图像统计性先验和稀疏性先验的图像模型研究[D]. 哈尔滨: 哈尔滨工业大学, 2014: 23- 25.

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

[22] 迟思琦, 陈文超, 张露, 等. 基于强背景干扰分离的三维地震信号纹理属性分析[ C]∥2018国际地球物理会议暨展览. [S.l: s.n], 2018: 4.

    Chi SQ, Chen WC, ZhangL, 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], 2018: 4.

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

[24] 屈光中. 基于稀疏表示的地震信号随机噪声压制与面波分离[D]. 合肥: 合肥工业大学, 2016: 20- 22.

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

[25] 蒋星达, 张伟, 王仔轩, 等. 基于总变分(TV)正则化约束的微地震井下速度模型校正[J]. 物探化探计算技术, 2018, 40(5): 559-564.

    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, 2018, 40(5): 559-564.

[26] 贾小宁. 基于傅立叶—全变差正则化的图像去卷积算法[D]. 长春: 吉林大学, 2011: 1- 5.

    Jia XN. Image deconvolution based on Fourier-total variation regularization[D]. Changchun: Jilin University, 2011: 1- 5.

[27] Dong WS, LiX, ZhangL, 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, 2011: 457- 464.

[28] Dong W S, Zhang L, Shi G M, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2013, 22(4): 1620-1630.

[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, 2014, 23(6): 2459-2472.

[30] 廖建尚, 王立国. 两类空间信息融合的高光谱图像分类方法[J]. 激光与光电子学进展, 2017, 54(8): 081002.

    Liao J S, Wang L G. Hyperspectral image classification method based on fusion with two kinds of spatial information[J]. Laser & Optoelectronics Progress, 2017, 54(8): 081002.

翁丽源, 周亚同, 何静飞, 李晓璐. 基于区域分割梯度直方图保持的地震信号去噪[J]. 激光与光电子学进展, 2020, 57(10): 101003. Liyuan Weng, Yatong Zhou, Jingfei He, Xiaolu Li. Seismic Signal Denoising Based on Region Segmentation Gradient Histogram Preservation[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101003.

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