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在视觉图像处理中，可用三高斯模型来模拟视网膜神经节细胞的感受野，这可以在一定程度上对图像信息，比如图像的边缘、细节等信息进行增强.但是在对大量的图像进行处理时，为了达到比较理想的效果，就需要人为地来改变模型中的相关参数，这是一个十分耗时的过程.基于此，本文提出一种基于忆阻交叉阵列的自适应三高斯模型.这种模型是在传统三高斯模型的基础上，根据所需处理图像的局部特征，利用忆阻交叉阵列的特性动态地改变模型参数，以达到对局部图像最优增强的目的，从而使整幅图像的增强效果更好.首先，根据图像的局部亮度信息来确定忆阻器所需施加的脉冲电压的极性以及宽度；然后，根据所得忆阻值得到对应模型中参数的值；最后，可以得到局部增强模板，从而实现增强.本文分别选取了彩色和灰度图像进行了测试，定性和定量实验结果均表明，这种改进的三高斯模型不仅能够对图像边缘进行有效的增强，而且还可以极大地提高图像的对比度和清晰度，为忆阻器在图像处理方面的应用提供了新方向.In visual image processing, there is a three-Gauss model used to simulate the receptive field of retinal ganglion cells, which can realize image enhancement to a certain extent, such as image edge and information about details. However, in dealing with a large number of image data, it is necessary to manually adjust the parameters of the three-Gauss model in order to achieve better results, which is a very tedious and time-consuming process. According to this, in this paper we propose an adaptive three-Gauss model based on memristive cross array. Memristor, whose resistance is controlled by size, polarity and power supply time of the power supply, is a kind of non-volatile component. Moreover, if the voltage applied to both ends of memristor is removed, it can still keep the resistance value when the power is off. Many studies show that when voltage pulses with the different amplitudes and the same width are applied to both ends of the memristor, the resistance will change continuously. This principle is adopted to realize image storage. Therefore, it makes use of the characteristics of memristor in this paper. The proposed model is based on the traditional three-Gauss model and changes the model parameters by using the dynamic characteristics of memristive cross array according to the local characteristics of the image to be processed, in order to achieve the purpose of local optimization and make the whole image obtain better enhancement effect. First of all, according to the local brightness information of the image, the polarity and the width of the pulse voltage required by the memristor are determined. Then, the values of the model parameters corresponding to the memristance can be obtained. Finally, the local enhancement template will be available to realize the enhancement. In this paper, the color and gray images are selected. The qualitative and quantitative experimental results show that the proposed adaptive three-Gauss model based on memristive cross array can not only effectively enhance the edge contour of the image, but also greatly improve the image contrast and clarity. Moreover, it provides a new direction for the application of memristor to image processing.
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Keywords:
- adaptive /
- three-Gaussian model /
- memristive crossbar array /
- image enhancement
[1] Li H, Wu W, Yang X M, Yan B Y, Liu K, Jeon G 2016 Acta Phys. Sin. 65 160701 (in Chinese) [李红, 吴炜, 杨晓敏, 严斌宇, 刘凯, Gwanggil Jeon 2016 65 160701]
[2] Bi G L, Xu Z J, Zhao J, Sun Q 2015 Acta Phys. Sin. 64 100701 (in Chinese) [毕国玲, 续志军, 赵建, 孙强 2015 64 100701]
[3] He K M, Sun J, Tang X O 2013 IEEE Trans. Pattern Anal. Mach. Intell. 35 1397
[4] Gianini G, Manenti A, Rizzi A 2014 J. Opt. Soc. Am. A 31 2663
[5] Gianini G, Rizzi A, Damiani E 2016 Inf. Sci 327 149
[6] Mccann J J, Parraman C, Rizzi A 2014 Front. Psychol. 5 1
[7] Wang Y F, Wang H Y, Yin C L, Dai M 2016 Neurocomputing 177 373
[8] Jobson D J, Rahman Z U, Woodell G A 1997 IEEE Trans. Image Process 6 965
[9] Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process 6 451
[10] Rodieck R W 1965 Vision Res. 5 583
[11] Li C Y, Xing P, Zhou Y X 1991 Vision Res. 31 1529
[12] Solomon S G, White A J, Martin P R 2002 J. Neurosci. 22 338
[13] Nolt M J, Kumbhani R D, Palmer L A 2004 J. Neurophysiol. 92 1708
[14] Xu Z L 2012 M. S. Thesis (Chengdu: University of Electronic Science and Technology of China) (in Chinese) [许子龙 2012 硕士学位论文 (成都: 电子科技大学)]
[15] Ahn H, Keum B, Kim D, Lee H S 2013 IEEE Int. Conf. Consum. Electron 2013 p153
[16] Zhang E H, Yang H Y, Xu M P 2015 Appl. Math. Inf. Sci. 9 411
[17] Jang I S, Lee T H, Ha H G, Ha Y H 2010 International Symposium on Optomechatronic Technologies Toronto, Canada, October 25-27, 2010 p1
[18] Wang Y, Yang J, Wang L D, Duan S K 2015 Acta Phys. Sin. 64 237303 (in Chinese) [王颜, 杨玖, 王丽丹, 段书凯 2015 64 237303]
[19] Wang L D, Drakakis E, Duan S K, He P F, Liao X F 2012 Int. J. Bifur. Chaos 22 1250205
[20] Hu X F, Duan S K, Wang L D, Liao X F 2011 Sci. China: Inf. Sci. 41 500 (in Chinese) [胡小方, 段书凯, 王丽丹, 廖晓峰 2011 中国科学: 信息科学 41 500]
[21] Duan S K, Hu X F, Dong Z K, Wang L, Mazumder P 2014 IEEE Trans. Neural Netw. Learn. Syst 26 1202
[22] Wang L D, Li H F, Duan S K, Huang T W, Wang H M 2015 Neurocomputing 171 23
[23] Agaian S S, Silver B, Panetta K A 2007 IEEE Trans. Image Process 16 741
[24] Agaian S S, Lentz K P, Grigoryan A M 2000 IASTED International Conference on Signal Processing Communication Marbella, Spain, September 19-22, 2000
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[1] Li H, Wu W, Yang X M, Yan B Y, Liu K, Jeon G 2016 Acta Phys. Sin. 65 160701 (in Chinese) [李红, 吴炜, 杨晓敏, 严斌宇, 刘凯, Gwanggil Jeon 2016 65 160701]
[2] Bi G L, Xu Z J, Zhao J, Sun Q 2015 Acta Phys. Sin. 64 100701 (in Chinese) [毕国玲, 续志军, 赵建, 孙强 2015 64 100701]
[3] He K M, Sun J, Tang X O 2013 IEEE Trans. Pattern Anal. Mach. Intell. 35 1397
[4] Gianini G, Manenti A, Rizzi A 2014 J. Opt. Soc. Am. A 31 2663
[5] Gianini G, Rizzi A, Damiani E 2016 Inf. Sci 327 149
[6] Mccann J J, Parraman C, Rizzi A 2014 Front. Psychol. 5 1
[7] Wang Y F, Wang H Y, Yin C L, Dai M 2016 Neurocomputing 177 373
[8] Jobson D J, Rahman Z U, Woodell G A 1997 IEEE Trans. Image Process 6 965
[9] Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process 6 451
[10] Rodieck R W 1965 Vision Res. 5 583
[11] Li C Y, Xing P, Zhou Y X 1991 Vision Res. 31 1529
[12] Solomon S G, White A J, Martin P R 2002 J. Neurosci. 22 338
[13] Nolt M J, Kumbhani R D, Palmer L A 2004 J. Neurophysiol. 92 1708
[14] Xu Z L 2012 M. S. Thesis (Chengdu: University of Electronic Science and Technology of China) (in Chinese) [许子龙 2012 硕士学位论文 (成都: 电子科技大学)]
[15] Ahn H, Keum B, Kim D, Lee H S 2013 IEEE Int. Conf. Consum. Electron 2013 p153
[16] Zhang E H, Yang H Y, Xu M P 2015 Appl. Math. Inf. Sci. 9 411
[17] Jang I S, Lee T H, Ha H G, Ha Y H 2010 International Symposium on Optomechatronic Technologies Toronto, Canada, October 25-27, 2010 p1
[18] Wang Y, Yang J, Wang L D, Duan S K 2015 Acta Phys. Sin. 64 237303 (in Chinese) [王颜, 杨玖, 王丽丹, 段书凯 2015 64 237303]
[19] Wang L D, Drakakis E, Duan S K, He P F, Liao X F 2012 Int. J. Bifur. Chaos 22 1250205
[20] Hu X F, Duan S K, Wang L D, Liao X F 2011 Sci. China: Inf. Sci. 41 500 (in Chinese) [胡小方, 段书凯, 王丽丹, 廖晓峰 2011 中国科学: 信息科学 41 500]
[21] Duan S K, Hu X F, Dong Z K, Wang L, Mazumder P 2014 IEEE Trans. Neural Netw. Learn. Syst 26 1202
[22] Wang L D, Li H F, Duan S K, Huang T W, Wang H M 2015 Neurocomputing 171 23
[23] Agaian S S, Silver B, Panetta K A 2007 IEEE Trans. Image Process 16 741
[24] Agaian S S, Lentz K P, Grigoryan A M 2000 IASTED International Conference on Signal Processing Communication Marbella, Spain, September 19-22, 2000
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