基于自回归移动平均模型的图像模糊消除机制

Study on Image Deblurring Mechanism Based on Autoregressive Moving Average Model

为了克服图像模糊消除算法不稳定与解模糊等难题,保证复原图像的细节信息清晰完整,并提高算法的运行效率,获取实时性,提出了神经网络融合自回归移动平均模型的图像模糊消除并行稳定机制。引入神经网络,基于突触权重系数,构造激活函数;再嵌入人工蜂群算法(Artificial Bees Colony,ABC),并以神经网络的均方误差函数设计适应度方程,由ABC算法训练神经网络,利用优化后的神经网络来获取自回归移动平均模型的参数;再将自回归移动平均优化模型引入模糊图像,以同时识别模糊函数与模糊图像;并对模糊函数进行相关定义,以消除算法不稳定性与解模糊问题;再对模糊图像进行反卷积,消除模糊。借助仿真实验来测试该机制的相关性能,结果表明:与其他模糊消除算法相比,该机制的运行速度更快,时耗最短;且该机制更稳定,模糊消除效果更好,复原图像的细节信息清晰可见。

In order to overcome the unstable with ambiguity blurring problem of these algorithm,as well as guarantee the clear and complete detail information of restoration image, and improve the computation speed of current image deblurring algorithm to achieve the goal of real time,the real-time stable mechanism for image deblurring based on the autoregressive moving average model is proposed. The active function is constructed by introducing the neural network and basing on the synaptic weights coefficient; then the fitness fnnction is designed by the mean square error function of neural network ; and embedding the artifieial bee colony al- gorithm （ABC-Arlificial Bees Colony） to train the neural network for finding the optimized weight value of neural network as well as the bias of active function to achieve global minimum;finally,the autoregressive moving average optimized model is designed to simultaneously identify fuzzy functions and fuzzy image to deconvolution the nonlinear deblurring image for eliminating the fuzzy. The performance of this algorithm is tested by simulation experiments. The results show that compared with other deblurring algnrithms, the running speed of this mechanism is faster, and time consuming is the shortest;as well as the deblurring effect is the best, the detail information of restoration image is clearly visible.