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Fast Image Restoration Algorithms Based on PDE Models Using Modified Hopfield Neural Network

机译:改进的Hopfield神经网络基于PDE模型的快速图像恢复算法

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Two image restoration algorithms based on modified Hop field neural network and variational partial differential equations (PDE) were proposed in our previous work [1, 2]. But the convergence rate of the proposed algorithms was slow. In this paper, we develop a fast update rule based on modified Hop field neural network (MHNN) of continuous state change and two fast image restoration algorithms. Experimental results show that, when compared with the previous algorithms, our proposed algorithms have better performance both in convergence rate and in image restoration quality.
机译:在我们之前的工作中提出了基于改进跳现场神经网络和变分部分微分方程(PDE)的两种图像恢复算法[1,2]。但是所提出的算法的收敛速度很慢。在本文中,我们基于连续状态变化和两个快速图像恢复算法的修改跳现场神经网络(MHNN)开发快速更新规则。实验结果表明,与先前的算法相比,我们所提出的算法在收敛速率和图像恢复质量方面具有更好的性能。

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