Image Restoration Based on Robust Error Function and Particle Swarm Optimization-BP Neural Network

Yinxue Zhang, Zhenhong Jia, Haijun Jiang, Zijian Liu
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引用次数: 8

Abstract

A new method for image restoration based on robust error function and BP neural network optimized with particle swarm optimization (PSO) is proposed in this paper. In this technique, BP neural network uses a robust error function as its error function, and then the neural network optimized with PSO. This method can minimize an evaluation function established based on an observed image. The proposed method takes into consideration point spread function (PSF) blurring as well as an additive random noise and obtains restoration image with more preserved image details. Experimental results demonstrate that the proposed new method can have a very high quality both in the visual qualitative performance and the quantitative performance than the traditional algorithms.
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基于鲁棒误差函数和粒子群优化- bp神经网络的图像恢复
提出了一种基于鲁棒误差函数和粒子群优化BP神经网络的图像恢复新方法。在该技术中,BP神经网络采用鲁棒误差函数作为误差函数,然后利用粒子群算法对神经网络进行优化。该方法可以最小化基于观测图像建立的评价函数。该方法考虑了点扩散函数(PSF)模糊和加性随机噪声,得到了保留图像细节更多的恢复图像。实验结果表明,与传统算法相比,该方法在视觉定性性能和定量性能上都具有很高的质量。
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