为了解决传统的Hopfield神经网络图像复原算法对噪声抑制和图像细节保护不能很好兼顾的问题，提出了一种基于改进的连续Hopfield神经网络和小波域隐Markov树(HMT)模型的复原算法。将小波域HMT模型作为图像小波系数统计关系的先验知识，并以正则化项的形式引入到神经网络模型中，最终利用Hopfield神经网络的能量收敛特性完成图像复原。同时提出了一种高度并行的网络权值矩阵计算方法，通过对模板图像进行算子操作，分批求取网络权值，避免了大型矩阵的乘法运算。实验结果表明，无论是对真实图像还是人工生成图像，算法复原的视觉效果均有明显改善，提高信噪比(ISNR)较传统同类算法增加了0.3 dB以上，达到了同时抑制噪声和保护图像细节的目的。

The traditional image restoration algorithms based on a Hopfield neural network are unable to compress the noise and protect the details at the same time. In order to solve the problem,a new algorithm based on the modified Hopfield neural network with a continuous state change and the wavelet domain Hidden Markov Tree (HMT) model is presented. The wavelet domain HMT model is utilized as the prior information about the statistical relationship between the two image wavelet coefficients, and is introduced into the neural network model by a regularization term. The final restoration image is obtained by using the energy convergence property of the Hopfield neural network. Furthermore, a highly-parallel weight matrix determination algorithm is proposed,and then the weight values are computed batch by batch through the operation to the pattern images to avoid the multiplication of large scale matrices. Experimental results demonstrate that the visual quality of the restoration result is improved evidently for either real images or artificial images, and the Improved Signal to Noise Ratio(ISNR) is improved more than 0.3 dB compared to that of the traditional algorithms. The objectives of compressing the noise and protecting the details are achieved at the same time.