针对常规最大类间方差法在多阈值图像分割中存在的运算量大、计算时间长、分割精度较低等问题,该文提出一种基于改进的自适应差分演化(JADE)算法的2维Otsu多阈值分割法。首先,为增强初始化种群的质量、提升控制参数的适应性,将混沌映射...针对常规最大类间方差法在多阈值图像分割中存在的运算量大、计算时间长、分割精度较低等问题,该文提出一种基于改进的自适应差分演化(JADE)算法的2维Otsu多阈值分割法。首先,为增强初始化种群的质量、提升控制参数的适应性,将混沌映射机制融入到JADE算法中;进而,通过该改进算法求解2维Otsu多阈值图像的最佳分割阈值;最终,将该算法与差分进化(DE), JADE,改进正弦参数自适应的差分进化(LSHADE-cn Ep Sin)以及增强的适应性微分变换差分进化(EFADE) 4种算法的2维Otsu多阈值图像分割进行比较。实验结果表明,与其它4种算法相比,基于改进JADE算法的2维Otsu多阈值图像分割在分割速度以及精度上均有较明显的改善。展开更多
Ghost imaging(GI)is thought of as a promising imaging method in many areas.However,the main drawback of GI is the huge measurement data and low signal-to-noise ratio.In this paper,we propose a novel mask-based denoisi...Ghost imaging(GI)is thought of as a promising imaging method in many areas.However,the main drawback of GI is the huge measurement data and low signal-to-noise ratio.In this paper,we propose a novel mask-based denoising scheme to improve the reconstruction quality of GI.We first design a mask through the maximum between-class variance(OTSU)method and construct the measurement matrix with speckle patterns.Then,the correlated noise in GI can be effectively suppressed by employing the mask.From the simulation and experimental results,we can conclude that our method has the ability to improve the imaging quality compared with traditional GI method.展开更多
文摘针对常规最大类间方差法在多阈值图像分割中存在的运算量大、计算时间长、分割精度较低等问题,该文提出一种基于改进的自适应差分演化(JADE)算法的2维Otsu多阈值分割法。首先,为增强初始化种群的质量、提升控制参数的适应性,将混沌映射机制融入到JADE算法中;进而,通过该改进算法求解2维Otsu多阈值图像的最佳分割阈值;最终,将该算法与差分进化(DE), JADE,改进正弦参数自适应的差分进化(LSHADE-cn Ep Sin)以及增强的适应性微分变换差分进化(EFADE) 4种算法的2维Otsu多阈值图像分割进行比较。实验结果表明,与其它4种算法相比,基于改进JADE算法的2维Otsu多阈值图像分割在分割速度以及精度上均有较明显的改善。
基金Project supported by the National Natural Science Foundation of China(Grant No.61627823)the Foundation for Excellent Young Talents of Jilin Province,China(Grant No.20190103010JH)+2 种基金the “13th Five-Year” Science and Technology Research Project of the Education Department of Jilin Province,China(Grant No.JJKH20190277KJ)the China Postdoctoral Science Foundation(Grant No.2018M641759)the Fundamental Research Funds for the Central Universities,China(Grant No.2412018QD002)
文摘Ghost imaging(GI)is thought of as a promising imaging method in many areas.However,the main drawback of GI is the huge measurement data and low signal-to-noise ratio.In this paper,we propose a novel mask-based denoising scheme to improve the reconstruction quality of GI.We first design a mask through the maximum between-class variance(OTSU)method and construct the measurement matrix with speckle patterns.Then,the correlated noise in GI can be effectively suppressed by employing the mask.From the simulation and experimental results,we can conclude that our method has the ability to improve the imaging quality compared with traditional GI method.
