BP Neural Network for Image Restoration Based on Grey Wolf Optimization Algorithm

YANG Shu-jie; YE Xia; LI Jun-shan

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BP Neural Network for Image Restoration Based on Grey Wolf Optimization Algorithm

更新时间：2026-09-04

- BP Neural Network for Image Restoration Based on Grey Wolf Optimization Algorithm
- Vol. 35, Issue 3, Pages: 19-22(2018)
- 作者机构：
  
  火箭军工程大学机构,陕西,西安,710025
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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杨书杰, 叶霞, 李俊山. 基于灰狼算法的BP神经网络图像恢复算法[J]. 微电子学与计算机, 2018, 35(3): 19-22, 27.

YANG Shu-jie, YE Xia, LI Jun-shan. BP Neural Network for Image Restoration Based on Grey Wolf Optimization Algorithm[J]. 2018, 35(3): 19-22.
杨书杰, 叶霞, 李俊山. 基于灰狼算法的BP神经网络图像恢复算法[J]. 微电子学与计算机, 2018, 35(3): 19-22, 27. DOI：

YANG Shu-jie, YE Xia, LI Jun-shan. BP Neural Network for Image Restoration Based on Grey Wolf Optimization Algorithm[J]. 2018, 35(3): 19-22. DOI：

摘要

提出了一种用灰狼算法优化的BP神经网络图像恢复算法.三层BP神经网络可以逼近任意复杂非线性关系

消除传统算法面临的高维方程计算和先验广义平稳假设约束

灰狼优化算法的全局搜索能力弥补BP神经网络性能过度依赖初始参数选择的缺点.实验结果表明

本文算法与维纳滤波算法和GA-BP算法相比具有更快的收敛速度和更高的复原精度.

Abstract

A BP neural network image restoration algorithm optimized by gray wolf algorithm is proposed. The three-layer BP neural network can approximate any complex nonlinear relationship

eliminate the high-dimensional equation calculation and the prior generalized stationary hypothesis constraint of the traditional algorithm. The global search ability of the gray wolf optimization algorithm makes up for the shortcomings of the BP neural network performance over-reliance on the initial parameter selection. And the experimental results show that the algorithm has faster convergence speed and higher recovery precision than Wiener filter algorithm and GA-BP algorithm.

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references

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