基于IWOA-BP神经网络图像复原
Image restoration based on IWOA-BP neural network
何昌 1詹道桦 1周倍 1罗志锋 1黄仁彬 1王晗1
作者信息
- 1. 广东工业大学机电工程学院,省部共建精密电子制造技术与装备国家重点实验室,广州 510006
- 折叠
摘要
针对传统复原算法在退化图像复原过程中存在明显滞后的问题,建立了 一种改进的鲸鱼算法(Im-proved Whale Optimization Algorithm,IWOA)-BP神经网络图像复原模型.首先,通过Tent混沌增强初始种群的均匀性和多样性;其次,采用非线性权重和改进的收敛因子,平衡算法的全局搜索与局部寻优能力;最后,结合Levy飞行策略更新个体位置,帮助算法跳出局部最优.随后采用经典图像数据,建立IWOA-BP模型.选取PSNR、SSIM和NMSE作为网络模型的评价指标,与BP、GWO-BP、WOA-BP进行对比.实验结果表明IWOA-BP模型图像复原视觉效果更好,提高了图像复原的质量.
Abstract
An Improved Whale Optimization Algorithm(IWOA)-BP neural network image restoration model was proposed to solve the problem of obvious lag in the process of restoring degraded images by traditional restoration algo-rithms.First,the uniformity and diversity of the initial population were enhanced by Tent chaos.Secondly,nonlinear weights and improved convergence factors are used to balance the global search and local optimization capabilities of the algorithm.Finally,the Levy flight strategy is combined to update the individual position to help the algorithm es-cape the local optimal.Then the IWOA-BP model is established by using the classical image data.PSNR,SSIM and NMSE were selected as the evaluation indexes of the network model,and compared with BP,GWO-BP and WOA-BP.The experimental results show that IWOA-BP model has better visual effect and improves the quality of image res-toration.
关键词
图像复原/BP神经网络/Tent混沌/Levy飞行/改进的鲸鱼算法Key words
image restoration/BP neural network/Tent chaos/Levy flies/Improved whale algorithm引用本文复制引用
基金项目
广东省季华实验室项目(X190071UZ190)
广东省自然科学基金(2021A1515011908)
出版年
2024
NETL
NSTL
万方数据