Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications.Because of the proportional relationship between image r...Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications.Because of the proportional relationship between image resolution and measurement time,when the image pixels are large,the measurement time increases,making it difficult to achieve real-time imaging.Therefore,a high-quality ghost imaging method based on undersampled natural-order Hadamard is proposed.This method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps,as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image information.We conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this method.This method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.展开更多
标准灰狼优化(grey wolf optimizer,GWO)算法存在局部探索和全局开发难以平衡等问题。针对此类问题,提出基于多策略结合的灰狼优化算法(multi-strategy grey wolf optimization,MSGWO)。首先,灰狼算法引入非线性收敛因子和Tent映射;然后...标准灰狼优化(grey wolf optimizer,GWO)算法存在局部探索和全局开发难以平衡等问题。针对此类问题,提出基于多策略结合的灰狼优化算法(multi-strategy grey wolf optimization,MSGWO)。首先,灰狼算法引入非线性收敛因子和Tent映射;然后,利用广泛学习、精英学习和协调学习三种策略,在GWO优化过程中协调工作;最后,利用轮盘赌进行策略选择,以获得更具多样性灰狼位置和更具全局代表性的个体。通过标准基准函数测试,采用算法变体进行对比。结果显示,MSGWO算法拥有较好的全局搜索、局部开发的平衡能力以及更快的收敛速度。在此基础上,利用MSGWO算法优化回声状态网络(echo state networks,ESN)超参数进行回归预测。实验表明平均绝对百分比误差为0.38%,拟合程度达到0.98,验证了MSGWO算法的优化性能。展开更多
基金the Science and Technology Development Plan Project of Jilin Province,China(Grant No.20220204134YY)the National Natural Science Foundation of China(Grant No.62301140)+3 种基金Project of the Education Department of Jilin Province(Grant Nos.JJKH20231292KJ and JJKH20240242KJ)Program for Science and Technology Development of Changchun City(Grant No.23YQ11)Innovation and Entrepreneurship Talent Funding Project of Jilin Province(Grant No.2023RY17)the Project of Jilin Provincial Development and Reform Commission(Grant No.2023C042-4).
文摘Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical applications.Because of the proportional relationship between image resolution and measurement time,when the image pixels are large,the measurement time increases,making it difficult to achieve real-time imaging.Therefore,a high-quality ghost imaging method based on undersampled natural-order Hadamard is proposed.This method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps,as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image information.We conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this method.This method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.
文摘标准灰狼优化(grey wolf optimizer,GWO)算法存在局部探索和全局开发难以平衡等问题。针对此类问题,提出基于多策略结合的灰狼优化算法(multi-strategy grey wolf optimization,MSGWO)。首先,灰狼算法引入非线性收敛因子和Tent映射;然后,利用广泛学习、精英学习和协调学习三种策略,在GWO优化过程中协调工作;最后,利用轮盘赌进行策略选择,以获得更具多样性灰狼位置和更具全局代表性的个体。通过标准基准函数测试,采用算法变体进行对比。结果显示,MSGWO算法拥有较好的全局搜索、局部开发的平衡能力以及更快的收敛速度。在此基础上,利用MSGWO算法优化回声状态网络(echo state networks,ESN)超参数进行回归预测。实验表明平均绝对百分比误差为0.38%,拟合程度达到0.98,验证了MSGWO算法的优化性能。
