提出一种基于非下采样轮廓波变换（NSCT）结合模糊域的噪声抑制和目标增强方法。使用NSCT将含噪声呐图像分解为不同的频率和方向响应子带，利用V-Bayes估计理论推导了模型的相应非线性二元阈值函数对NSCT系数的子带进行去噪。结合领域信息和空间信息构造模糊特征，再构建出模糊隶属度收缩函数对降噪后的NSCT分量执行二次降噪，最后以重建降噪后的声呐图像数据，使用综合数据和效果图示例来证明所提出的方法能够有效的去除声呐图像噪声，且优于基于小波变换的阈值去噪方法。
A non - subsampled contourlet transform (NSCT) combined with adaptive threshold is proposed for noise suppression and target enhancement. First, NSCT can be used to decompose noise-containing sonar images into different frequency and directional response subbands. Then, we use the adaptive threshold operator to de-noise the subband of the NSCT coefficient. Combined with the feature of field information and spatial information to construct the fuzzy, to build up the fuzzy membership degree after the contraction function of noise reduction of NSCT secondary noise component implementation, and finally to rebuild the sonar image data of the noise reduction after using sample data to prove that the proposed method can effectively remove noise sonar image, and is superior to the threshold denoising method based on wavelet transform.
2022,44(7): 138-141 收稿日期：2021-01-08
DOI：10.3404/j.issn.1672-7649.2022.07.027
分类号：TP391.41
基金项目：国防基础科研计划项目（JCKY2017414C002）；国家自然科学基金资助项目（11574120）；江苏省产业前瞻与共性关键技术（BE2018103）
作者简介：马启星(1997-),女,硕士研究生,研究方向为现代测控技术
参考文献：
[1] 陈鹏, 张建伟. 结合核函数与非线性偏微分方程的图像去噪方法[J]. Computer Science, 2018, 45(11): 278–282
[2] WANG Y, WANG H, YIN C, et al. Biologically inspired image enhancement based on Retinex[J]. Neurocomputing, 2016, 177(177): 373–384
[3] 杨蕴, 李玉, 王玉. 一种数学形态学的量子图像去噪方法[J]. 遥感信息, 2018, 33(2): 17–25
[4] 李利, 杨恢先, 何雅丽, 等. 结合FT与NSCT的自适应阈值去噪算法[J]. 计算机工程, 2012, 38(17): 238–241
[5] 姜三平, 郝晓剑. 应用小波系数GSM统计模型的混合傅里叶-小波图像降噪[J]. 中国图象图形学报, 2009, 14(3): 448–451
[6] MA Guangsheng, ZHAO Jiman. Quaternion ridgelet transform and curvelet transform. [J].Adoances in Applied Clifford Algebras, 2018, 28(4): 1-21.
[7] SHAHDOOSTI H R, HAZAVEI S M. Image denoising in dual contourlet domain using hidden Markov tree models[J]. Digital Signal Processing, 2017: 17–29
[8] STARCK J L , CANDES E J , DONOHO D L . The curvelet transform for image denoising[J]. IEEE Transaction on image processing a publication of the IEEE signal processing society, 2002, 11(6): 670-684.
[9] DA, C, ARTHUR, et al. The nonsubsampled contourlet transform: theory, design, and applications.[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089–3101
[10] 吴炜, 杨晓敏, 陈默, 等. 基于改进的非下采样Contourlet变换的超分辨率复原算法[J]. 光学学报, 2009, 29(6): 1493–1501
[11] HUANG Yun-hu, CHEN De-wang. Image fuzzy enhancement algorithm based on contourlet transform domain[J]. Multimedia Tools and Applications, 2019.
[12] 杨建雷. 基于非采样Contourlet变换和模糊逻辑的图像增强算法研究[D]. 保定:河北大学, 2011.
[13] FRAYSSE A, RODET T . A measure-theoretic variational Bayesian algorithm for large dimensional problems[J]. SIAM Journal on Imaging Sciences, 2013, 7(4).
[14] 霍冠英, 李庆武, 王敏, 等. Curvelet域贝叶斯估计侧扫声呐图像降斑方法[J]. 仪器仪表学报, 2011, 32(1): 170–177
[15] 李庆武, 马国翠, 霍冠英, 等. 基于NSCT域边缘检测的侧扫声呐图像分割新方法[J]. 仪器仪表学报, 2013(8): 1795–1801
[16] LYU Zhiyu, ZHANG Chengkun, HAN Min. A nonsubsampled countourlet transform based CNN for real image denoising[J]. Signal Processing:Image Communication, 2020: 82
[17] LU Huimin, YAMAWAKI A, Serikawa S. Curvelet approach for deep-sea sonar image denoising, contrast enhancement and fusion[J]. Journal of International Council on Electrical Engineering, 2013, 3(3).
