Denoising method of intensity image for laser active imaging system

Wu Kun; Zhang Hexin; Meng Fei; Chen Cong

According to the characteristics of laser active imaging and practical application,a new image denoising algorithm based on homomorphic filtering and dual tree complex wavelet transform (DTCWT) was proposed. Firstly, speckle noise was converted from multiplicative noise to additive noise by homomorphic transform. Secondly, the noise image was decomposed with the Q-shift DTCWT, then wavelet coefficients were revised by Bayes adaptive threshold method. Finally, inverse transforms were carried out and the denoised intensity image was obtained. The algorithm proposed had approximate shift- invariant, good directional selectivity and perfect reconstruction. The image signal to noise ratio (SNR), peak signal to noise ratio (PSNR) and the run time were applied to estimate the denoising effect. Experimental results show that the proposed algorithm has advanced denoising performance in laser active imaging and great efficiency in computation. Meanwhile, the detail of image is well protected.

1. Department of Automation,The Second Artillery Engineering University,Xi'an 710025,China

Received Date: 2013-01-06

Rev Recd Date: 2013-02-14

Publish Date: 2013-09-25

Key words:

Abstract: According to the characteristics of laser active imaging and practical application,a new image denoising algorithm based on homomorphic filtering and dual tree complex wavelet transform (DTCWT) was proposed. Firstly, speckle noise was converted from multiplicative noise to additive noise by homomorphic transform. Secondly, the noise image was decomposed with the Q-shift DTCWT, then wavelet coefficients were revised by Bayes adaptive threshold method. Finally, inverse transforms were carried out and the denoised intensity image was obtained. The algorithm proposed had approximate shift- invariant, good directional selectivity and perfect reconstruction. The image signal to noise ratio (SNR), peak signal to noise ratio (PSNR) and the run time were applied to estimate the denoising effect. Experimental results show that the proposed algorithm has advanced denoising performance in laser active imaging and great efficiency in computation. Meanwhile, the detail of image is well protected.

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Reference (29)

[1]
[2]	Wang Xiangke, Zhang Hui, Zheng Zhiqiang. Laser active imaging seeker design technology[J]. Infrared and Laser Engineering, 2008, 37(5): 797-801. (in Chinese) 王祥科,张辉,郑志强. 激光主动成像图像制导导引头设计[J]. 红外与激光工程, 2008, 37(5): 797-801.
[3]
[4]	Lee J S. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE Trans Pattern Analysis and Machine Intell, 1980, 2(2): 165-168.
[5]	Kuan D T, Sawchuk A A. Adaptive noise smoothing filter for signal- dependent noise[J]. IEEE Trans Pattern Analysis and Machine Intell, 1985, 7(2): 165-177.
[6]
[7]	Frost V S, Stiles J A, Shanmugan K S, et al. A mode for radar images and its application to adaptive digital filtering of multiplicative noise[J]. IEEE Trans Pattern Analysis and Machine Intell, 1982, 4(2): 157-165.
[8]
[9]
[10]	Li Xiaofeng, Xu Jun, Luo Jijun, et al. Noise analyzing and denoising of intensity image for laser active imaging system[J]. Infrared and Laser Engineering, 2010, 20(2): 332-337. (in Chinese) 李晓峰, 徐军,罗积军, 等. 激光主动成像图像噪声分析与抑制[J]. 红外与激光工程, 2010, 20(2): 332-337.
[11]	Xu Yibin, Xu Jun, Zhao Falin, et al. Laser active image-denoising method based on wavelet transform[J]. High Power Laser and Particle Beams, 2009, 21(12): 1786-1790. (in Chinese) 许毅玢, 徐军,赵法林, 等. 基于小波变换的激光主动成像图像去噪方法[J]. 强激光与粒子束, 2009, 21(12): 1786-1790.
[12]
[13]
[14]	Gagnon L, Smaili F D. Speckle noise reduction of airborne sar images with symmetric daubechies wavelets[C]//SPIE, 1996, 2759: 1424.
[15]	Kingsbury N G. Complex wavelets for shift invariant analysis and filtering of signals[J]. Journal of Applied and Computational Harmonic Analysis, 2001, 10(3): 234-253.
[16]
[17]
[18]	Kingsbury N G. Design of q-shift complex wavelets for image processing using frequency domain energy minimization[C]//Proc IEEE Int Conf Image Processing, 2003: 1013-1016.
[19]
[20]	Selesnick I W. The design of approximate Hilbert transform pairs of wavelet bases[J]. IEEE Trans Signal Processing,2002, 50(5): 1144-1152.
[21]	Wang Hongxia, Cheng Lizhi, Wu Yi. The design of Q-shift wavelet transform and its application in image denoising[J]. Signal Processing, 2005, 21(5): 520-524. (in Chinese) 王红霞,成礼智,吴诩. Q-shift复小波的一种新型构造方法及其在图像去噪中的应用[J]. 信号处理, 2005, 21(5): 520-524.
[22]
[23]	Li Ziqin, Li Qi, Wang Qi. Noise characteristic in active laser imaging system by statistic analysis[J]. Chinese Journal of Lasers, 2004, 31(9): 1081-1085. (in Chinese) 李自勤,李琦,王骐. 由统计特性分析激光主动成像系统图像的噪声性质[J]. 中国激光, 2004, 31(9): 1081-1085.
[24]
[25]	Yen J C, Chang S A, Chang S A. A new criterion for outomatic multi-level threasholding[J]. IEEE Trans on Image Processing, 1995, 4(3): 370-378.
[26]
[27]
[28]	Sweldens W. The lifting scheme: a construction of second generation wavelets[J]. SIAM Journal on Mathematical Analysis, 1998, 29(2): 511-546.
[29]	Wang Lei, Xu Zhiyong, Zhang Qiheng, et al. Detection sensitivity analysis of underwater blue-green laser imaging system[J]. Infrared and Laser Engineering, 2012, 41(1): 79-84. (in Chinese)

Denoising method of intensity image for laser active imaging system

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