基于新符号函数与盲源分离的光子计数图像去噪方法

在10-4 lx环境下, 由多像素光子计数探测器利用光子计数原理点阵扫描得到光子计数图像。为了呈现更多细节, 获得更高清晰度的图像, 首先采用Bayes-Shink阈值及改进的新符号函数对光子计数图像进行处理, 然后在图像重构阶段将低频系数置零, 以处理后的高频系数进行图像重构, 并将其设置为虚拟通道, 使观测信号的个数与信号源个数相同, 从而满足快速独立成分分析无噪分离模型, 最后实现光子计数图像和噪声的盲源分离。实验结果表明, 该算法与小波软、硬阈值算法和符号函数算法相比, 图像的峰值信噪比分别提高了16.39%、10.18%、5.20%。同时, 滤除噪声后的图像较好地保护了边缘细节, 视觉效果良好。

Abstract

The photon counting image is scanned by multi-pixel photon counting detector point by point under the environment of 10-4 lx according to the principle of photon counting. To present more details and get a high definition image, the Bayes-Shrink threshold and the improved new symbol functions are used to realize image de-noising preprocessing first. Then, in the stage of image reconstruction, the low-frequency coefficients are set to zero to reconstruct the image with high-frequency coefficients after processing and it is set as a virtual channel to make the number of observation signals equal to the number of signal sources. Finally , the fast independent component analysis noiseless separation model is used to separate the photon counting image from noise by blind source separation. The experimental results show that the peak signal to noise ratios of the image are improved by 16.39%, 10.18%, 5.20%, respectively, compared with the soft, hard and new symbol function de-noising algorithm. The image after removing noise is also good to protect the edge details, and the visual effect is good.

参考文献

[1] 季中杰. APD单光子计数成像实验研究［D］. 南京: 南京理工大学, 2012: 4-7.

Ji Z J. The research of APD single photon counting imaging experiment［D］. Nanjing:Nanjing University of Science and Technology, 2012: 4-7.

[2] 常超. AlGaN雪崩光电二极管噪声特性测试及分析［D］. 上海: 中国科学院研究生院(上海技术物理研究所), 2015: 22-25.

Chang C. Noise characteristic of AlGaN avalanche photodiodes measurement and analysis［D］. Shanghai: Graduate School of Chinese Academy of Sciences (Shanghai Institute of Technical Physics), 2015: 22-25.

[3] 王芳. 雪崩光电二极管的噪声测试及应用研究［D］. 西安: 西安电子科技大学, 2011: 39-47.

Wang F. Research on the measurement and application of avalanche photodiodes noise［D］. Xi′an: Xidian University, 2011: 39-47.

[4] 柳俊彦. 微光图像的轮廓编码研究［D］. 北京: 北京交通大学, 2010: 18-26.

Liu J Y. Research on contour grouping for low-light images［D］. Beijing: Beijing Jiaotong University, 2010: 18-26.

[5] 吴微, 彭华, 周正康. 一种改进的FastICA算法及其在含噪盲源分离中的应用［J］. 信息工程大学学报, 2013, 14(6): 708-712.

Wu W, Peng H, Zhou Z K. Improved FastICA algorithm and its application in noisy blind sources separation［J］. Journal of Information Engineering University, 2013, 14(6): 708-712.

[6] 孟宗, 马钊, 刘东, 等. 基于小波半软阈值消噪的盲源分离方法［J］. 中国机械工程, 2016, 27( 3): 337-342.

Meng Z, Ma Z, Liu D, et al. Blind source separation based on wavelet semi-soft threshold denoising［J］. China Mechanical Engineering, 2016, 27(3): 337-342.

[7] 蔡伟华, 何选森. 基于UWT和独立分量分析的含噪盲源分离［J］. 计算机工程与应用, 2016, 52(16): 180-185.

Cai W H,He X S. Noisy blind source separation based on undecimated wavelet transform and independent component analysis［J］. Computer Engineering and Applications, 2016, 52(16): 180-185.

[8] 赵奎, 黄高明. 基于二次小波去噪的FastICA盲源分离研究［J］. 舰船电子工程, 2015, 35(6): 36-40.

Zhao K, Huang G M. FastICA blind source separation based on secondary-wavelet denoising［J］. Ship Electronic Engineering, 2015, 35(6): 36-40.

[9] 王杏. 带噪混叠语音信号盲分离方法研究［D］. 北京: 北京交通大学, 2014: 20-27.

Wang X. The study on blind separation of noisy speech mixtures［D］. Beijing: Beijing Jiaotong University, 2014: 20-27.

[10] 贾伟宽, 赵德安, 阮承治, 等. 苹果夜视图像小波变换与独立成分分析融合降噪方法［J］. 农业机械学报, 2015, 46(9): 9-17.

Jia W K, Zhao D A, Ruan C Z, et al. Combined method for night vision image denoising based on wavelet transform and ICA［J］. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(9): 9-17.

[11] 吴微. 含噪盲源分离算法的研究及其在水声信号中的应用［D］. 郑州: 解放军信息工程大学, 2014: 104-110.

Wu W. Research on noisy blind source separation algorithm and its application in underwater acoustic signals［D］. Zhengzhou: PLA Information Engineering University, 2014: 104-110.

[12] Li J, Cheng C K, Jiang T Y, et al. Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation［J］. IEEE Transactions on Dielectrics and Electrical Insulation, 2012, 19(2): 543-549.

[13] 江虹, 苏阳. 一种改进的小波阈值函数去噪方法［J］. 激光与红外, 2016, 46(1): 119-122.

Jiang H, Su Y. Denoising method based on improved wavelet threshold function［J］. Laser & Infrared, 2016, 46(1): 119-122.

[14] Donoho D L, Johnstone J M. Ideal spatial adaptation by wavelet shrinkage［J］. Biometrika, 1994, 81(3): 425-455.

[15] Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression［J］. IEEE Transactions on Image Processing, 2000, 9(9): 1532-1546.

[16] 李东明, 盖梦野, 李超然, 等. 基于小波域的Contourlet变换法的自适应光学图像去噪算法研究［J］. 激光与光电子学进展, 2015, 52(11): 111001.

Li D M, Gai M Y, Li C R, et al. Research on adaptive optics image denoising algorithm based on the wavelet-based contourlet transform［J］. Laser & Optoelectronics Progress, 2015, 52(11): 111001.

[17] 郭武, 王润生, 张鹏, 等. 基于独立分量分析的图像去噪研究［J］. 信号处理, 2008, 24(3): 381-385.

Guo W, Wang R S, Zhang P, et al. Image denoising based independent component analysis［J］.Signal Processing, 2008, 24(3): 381-385.

[18] 赵常兵. 基于盲分离的图像去噪技术研究［D］. 哈尔滨: 哈尔滨工程大学, 2015: 49-61.

Zhao C B. Research of image denoising based on blind source separation［J］. Harbin: Harbin Engineering University, 2015: 49-61.

[19] Oja E, Yuan Z J. The FastICA algorithm revisited: convergence analysis［J］. IEEE Transactions on Neural Networks, 2006, 17(6): 1370-1381.

[20] Novey M, Adali T. On extending the complex FastICA algorithm to noncircular sources［J］. IEEE Transactions on Signal Processing, 2008, 56(5): 2148-2154.

王炫, 尹丽菊, 高明亮, 申晋, 邹国峰, 胡浩东, 仲红玉. 基于新符号函数与盲源分离的光子计数图像去噪方法[J]. 激光与光电子学进展, 2018, 55(10): 101103. Wang Xuan, Yin Liju, Gao Mingliang, Shen Jin, Zou Guofeng, Hu Haodong, Zhong Hongyu. De-Noising Method of Photon Counting Image Based on New Symbol Function and Blind Source Separation[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101103.

基于新符号函数与盲源分离的光子计数图像去噪方法

关于本站 Cookie 的使用提示

全站搜索

基于新符号函数与盲源分离的光子计数图像去噪方法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索