一种噪声混沌神经网络图像去噪方法

Leipo Yan, Lipo Wang, Kim-Hui Yap
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引用次数: 20

摘要

本文提出了一种新的基于噪声混沌神经网络(NCNN)的图像去噪方法。将图像去噪的贝叶斯框架重新表述为使用连续松弛标记的约束优化问题。将模拟退火技术与Hopfield神经网络(HNN)相结合的NCNN用于求解优化问题。它有效地克服了HNN可能引起的局部极小问题。实验结果表明,NCNN可以提供较好的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A noisy chaotic neural network approach to image denoising
This paper presents a new approach to address image denoising based on a new neural network, called noisy chaotic neural network (NCNN). The original Bayesian framework of image denoising is reformulated into a constrained optimization problem using continuous relaxation labeling. The NCNN, which combines the simulated annealing technique with the Hopfield neural network (HNN), is employed to solve the optimization problem. It effectively overcomes the local minima problem which may be incurred by the HNN. The experimental results show that the NCNN could offer good quality solutions.
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