|
|
|
| Image restoration based on stochastic resonance mechanism of
FitzHugh-Nagumo neuron |
| XUE Ling-yun1,2, DUAN Hui-long1, XIANG Xue-qin2, FAN Ying-le2 |
| 1. Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China;
2. Institute of Biomedical Engineering and Instrument, Hangzhou Dianzi University, Hangzhou 310018, China |
|
|
|
Abstract Based on stochastic resonance(SR) mechanism, the quality of low signalnoiseratio image was improved by adding noise energy. According to analyzing suprathreshold stochastic resonance and threshold working characteristic of FitzhughNagumo(FHN) neuron model at phase space, the model was simplified. By choosing the peak signaltonoise ratio(PSNR) as estimation function of the image restoration, a selfadaptive optimized algorithm of image restoration processing based on RS mechanism was introduced. A noisy mountain color image, LED chip image and so on, were chosen as experimental objects, to compare simulation result of image restoration with different algorithm, stochastic resonance restoration, mean filter restoration and Wiener filter restoration. The results indicate that SR image restoration method is better in noise inversion, image detail restoration and color information restoration. Along with the increasing of noise intensity, the peak signaltonoise ratio of SR restoration images change less than others. This method has good robustness.
|
|
Published: 16 July 2010
|
|
|
基于FitzHughNaguno神经元随机共振机制的图像复原
应用随机共振机制,借助噪声能量实现图像复原,改善低信噪比图像的输出质量.通过分析FitzHugh-Nagumo(FHN)神经元的阈上随机共振机理,以及在相平面上的阈值工作特性,对FHN神经元模型进行约简,以峰值信噪比（PSNR）为图像复原的评价函数,提出基于随机共振的自适应最优图像复原算法.以含噪mountain彩色图像和LED芯片图像为实验对象,与均值滤波、维纳滤波等图像复原算法进行仿真对比研究.结果表明：随机共振方法较好地抑制了噪声、恢复了图像细节和色彩信息,且随着噪声的增强,随机共振方法复原图像的峰值信噪比变化较小,该方法具有较好的鲁棒性.
|
|
|
［1］ TAN S, YOUNG R C, BUDGETT D M, et al. Performance comparison of a linear parametric noise estimation Wiener filter and nonlinear joint transform correlator for realistic clutter backgrounds［J］. Optics Communication, 2000, 182: 8390.
［2］ DAUBECHIES I, TESCHKE G. Variational image restoration by means of wavelets: simultaneous decomposition, deblurring and denoising［J］. Applied and Computational Harmonic Analysis, 2005, 19(1): 116.
［3］ 尚晓清,王军锋,宋国乡.基于Bayesian估计和Wiener滤波的阈值去噪方法［J］.光子学报,2003,32(7): 889891.
SHANG Xiaoqing, WANG Junfeng, SONG Guoxiang. Adaptive wavelet thresholding denoising method based on Bayesian［J］. Acta Photonica Sinica, 2003, 32(7): 889891.
［4］ BENZI R, SUTERA A, VULPLANI A. The mechanism of stochastic resonance［J］. Journal of Physics A, 1981, 14(11): 453457.
［5］ 刘勇, 鲍荣浩,段法兵.参数调节随机共振与信号处理的研究：在四进制基带PAM信号传输中的应用［J］.浙江大学学报:工学版,2005,39(8): 12601264.
LIU Yong, BAO Ronghao, DUAN Fabing. Study of parameterinduced stochastic resonance and signal processing: Application in baseband quaternary PAM signals transmission［J］. Journal of Zhejiang University: Engineering Science, 2005, 39(8): 12601264.
［6］ 杨祥龙,江波,吴为麟,等.工程信号测量中的自适应随机共振应用［J］.电路与系统学报,2003,8(2): 104107.
YANG Xianglong, JIANG Bo, WU Weilin, et al. Application of adaptive stochastic resonance in actual engineering signal detection［J］. Journal of Circuits and Systems, 2003, 8(2): 104107.
［7］ YE Qinghua, HUANG Haining, ZHANG Chunhua. Image enhancement using stochastic resonance ［C］∥ Proceedings of the 2004 International Conference on Image Processing (ICIP 2004). Singapore： IEEE, 2004: 263266.
［8］ SASAKI H, SAKANE S, ISHIDA T, et al. Subthreshold noise facilitates the detection and discrimination of visual signals［J］. Neuroscience Letters, 2008, 436(2): 255258.
［9］ MARKS R, THOMPSON H, EISHARKAWI M, et al. Stochastic resonance of a threshold detector: image visualization and explanation ［C］∥ 2002 IEEE International Symposium on Circuits and Systems. Seattle： IEEE, 2002: IV521 IV523.
［10］ HONGLER M, MENESES Y, BEYELER A. The resonant retina: exploiting vibration noise to optimally detect edges in an image［J］. IEEE Transactions on Pattern Analysis and Machine Intelligent, 2003, 25(9): 10511062.
［11］ XU Bohou, DUAN Fabing, FRANCOIS C. Comparison of aperiodic stochastic resonance in a bistable system realized by adding noise and by tuning system parameters［J］. Physical Review E, 2004, 69(6 Pt 1): 061110.
［12］ NOZAKI D, YAMAMOTO Y. Enhancement of stochastic resonance in a FitzHughNagumo neuronal model driven by colored noise［J］. Physics Letters A, 1998, 243(5/6): 281287. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
