针对图像复原方法普遍运算量大的问题，提出了一种利用细胞神经网络进行图像复原的新方法，并首先提出了易于硬件实现的基于边缘方向判据的正则化复原方法；然后通过细胞神经网络的能量函数设计合适的网络参数来对该正则化函数进行细胞神经网络实现。仿真结果表明，该新方法是有效的，复原效果优于有约束的最小二乘复原法和已有的细胞神经网络图像复原法，而且由于细胞神经网络的并行性和硬件易实现性，使该新方法可以实时进行图像复原。

Aiming at the real time image restoration, a novel image restoration algorithm using cellular neural networks is proposed in this paper. First, based on the classic regularized image restoration algorithm, the edge direction constraint is introduced with easy hardware implementation. Then, suitable template parameters of cellular neural networks are designed based on the CNN energy function. The cellular neural network is applied to implement the regularization function of the traditional model of degraded image. Simulation results show the efficiency of the new algorithm, its restoration results are better than the least square image restoration method with constraints and another image restoration algorithm base on CNN. Because of the easy hardware implementation and parallel realization, the real time image restoration can be realized with the new algorithm.