动态核的形态分解联想算法及抗随机噪声研究

Morphological decomposing associative memory based on dynamic kernel and its robust analysis for random noise

在灰度图像分解算法和动态核形态联想记忆网络的基础上,提出了一种新的联想记忆算法——动态核的形态分解联想算法。该方法显著地提高了联想记忆抗随机噪声的能力,较好地解决了灰度图像在含噪时的联想记忆和识别的问题,从而给出了一种恢复含噪灰度图像的途径,并把该方法推广到了彩色图像的处理。通过实验,验证了该方法的良好性能,取得了理想的结果。

A new kind of associative memories method is found through the decomposing of gray-scale images, combining the method of morphological associative memories based on dynamic kernel, which improves its competence against random noise. With the method, the association and recognition of gray-scale images with random noise is settled. Accordingly, a better way to resume gray-scale images with noise is offered. Moreover, the method is popularized into the settlement and application of color-scale images. Our experimental results demonstrate the effectiveness of this approach.