刘欢, 欧阳春娟, 肖根福. 免疫机理的图像去噪方法研究[J]. 微电子学与计算机, 2016, 33(3): 77-80.
引用本文: 刘欢, 欧阳春娟, 肖根福. 免疫机理的图像去噪方法研究[J]. 微电子学与计算机, 2016, 33(3): 77-80.
LIU Huan, OUYANG Chun-juan, XIAO Gen-fu. Research of Image Denoising Method with Immune Mechanism[J]. Microelectronics & Computer, 2016, 33(3): 77-80.
Citation: LIU Huan, OUYANG Chun-juan, XIAO Gen-fu. Research of Image Denoising Method with Immune Mechanism[J]. Microelectronics & Computer, 2016, 33(3): 77-80.

免疫机理的图像去噪方法研究

Research of Image Denoising Method with Immune Mechanism

  • 摘要: 为了有效降低图像中的噪声, 提出一种基于免疫机理的图像去噪方法.该方法通过模拟生物免疫系统中抗体、抗原间信息处理机制来去除图像中的噪声.一方面考虑中心像素与其一定邻域范围内像素间的抑制作用; 另一方面引入中心像素与邻域外其他像素间存在的相互刺激作用; 将两者结合加权决定该像素灰度估计值, 获得去噪后的图像.对比实验结果表明: 与其他方法相比, 此方法获得更高的峰值信噪比(PSNR)和等效视数(ENL), 具有更好的视觉效果, 能更好地去除图像中的噪声并保留图像细节信息.该方法为图像去噪提供了一条新的途径, 对今后的研究具有重要的参考价值.

     

    Abstract: Aiming at denoising image more effectively, an image denoising method based on immune mechanism is proposed. It removes the noise by means of simulating the information processing mechanism correlated to the antibody and antigen in biological immune system. On the one hand, the inhibition between the central pixel and its neighborhood pixels within a certain range is considered. On the other hand, involving the simulation interconnected the central pixel with the others outside the neighborhood range, and then in virtue of the combination of the above two aspects and the weighted calculation as well, a nice denoised image can be obtained by means of estimating the pixel gray. Compared with other methods, the contrast experiment results indicate that the proposed approach can get higher PSNR and ENL with better visual quality, and reduce noise more effectively as well as preserve image detail information very well. It provides a new way and gives an important reference value for future research of image denoising.

     

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