Wavefront coding （WFC） is used to extend the field depth of an incoherent optical system by employing a phase mask on the pupil. We uses a Fisher information （FI） metric based optimization method to design a phase...Wavefront coding （WFC） is used to extend the field depth of an incoherent optical system by employing a phase mask on the pupil. We uses a Fisher information （FI） metric based optimization method to design a phase mask by taking the modulation transfer function （MTF） of the practical optical system into consid- eration. This method can modulate the wavefront so that the point spread function and optical transfer function are insensitive to the object distance. The simulation results show that the optimized phase mask based on the proposed method can further improve the defocusing image quality while maintaining the focusing image quality.展开更多
The wavefront coding technique is used to enlarge the depth of field（DOF）of incoherent imaging systems. The key to wavefront coding lies in the design of suitable phase masks.To date,numerous kinds of phase masks ar...The wavefront coding technique is used to enlarge the depth of field（DOF）of incoherent imaging systems. The key to wavefront coding lies in the design of suitable phase masks.To date,numerous kinds of phase masks are proposed.However,further understanding is needed regarding phase mask with its phase function being in a standard sinusoidal form.Therefore,the characteristics of such a phase mask are studied in this letter.Deriving the defocused optical transfer function（OTF）analytically proves that the standard sinusoidal phase mask is effective in extending the DOF,and actual experiments confirm the numerical results.At the same time,with the Fisher information as a criterion,the standard sinusoidal phase mask shows a higher tolerance to focus errors（especially severe focus errors）than the classical cubic phase mask.展开更多
社团结构划分对于分析复杂网络的统计特性非常重要,以往研究往往侧重对无向网络的社团结构挖掘,对新兴的微信朋友圈网络、微博关注网络等涉及较少,并且缺乏高效的划分工具。为解决传统社团划分算法在大规模有向社交网络上无精确划分模...社团结构划分对于分析复杂网络的统计特性非常重要,以往研究往往侧重对无向网络的社团结构挖掘,对新兴的微信朋友圈网络、微博关注网络等涉及较少,并且缺乏高效的划分工具。为解决传统社团划分算法在大规模有向社交网络上无精确划分模拟模型,算法运行效率低,精度偏差大的问题。该文从构成社团结构最基础的三角形极大团展开数学推导,对网络节点的局部信息传递过程进行建模,并引入概率图有向矢量计算理论,对有向社交网络中具有较大信息传递增益的节点从数学基础创造性地构建了有向传递增益系数(Information Transfer Gain,ITG)。该文以此构建了新的有向社团结构划分效果的目标函数,提出了新型有向网络社团划分算法ITG,通过在模拟网络数据集和真实网络数据集上进行实验,验证了所提算法的精确性和新颖性,并优于Fast GN,OSLOM和Infomap等经典算法。展开更多
基金supported by the National Natural Science Foundation of China(No.60777002)Ningbo Science and Technology Bureau(No.2008A610035).
文摘Wavefront coding （WFC） is used to extend the field depth of an incoherent optical system by employing a phase mask on the pupil. We uses a Fisher information （FI） metric based optimization method to design a phase mask by taking the modulation transfer function （MTF） of the practical optical system into consid- eration. This method can modulate the wavefront so that the point spread function and optical transfer function are insensitive to the object distance. The simulation results show that the optimized phase mask based on the proposed method can further improve the defocusing image quality while maintaining the focusing image quality.
基金supported by the West Light Foundation of the Chinese Academy of Sciences under GrantNo.J11-002
文摘The wavefront coding technique is used to enlarge the depth of field（DOF）of incoherent imaging systems. The key to wavefront coding lies in the design of suitable phase masks.To date,numerous kinds of phase masks are proposed.However,further understanding is needed regarding phase mask with its phase function being in a standard sinusoidal form.Therefore,the characteristics of such a phase mask are studied in this letter.Deriving the defocused optical transfer function（OTF）analytically proves that the standard sinusoidal phase mask is effective in extending the DOF,and actual experiments confirm the numerical results.At the same time,with the Fisher information as a criterion,the standard sinusoidal phase mask shows a higher tolerance to focus errors（especially severe focus errors）than the classical cubic phase mask.
文摘社团结构划分对于分析复杂网络的统计特性非常重要,以往研究往往侧重对无向网络的社团结构挖掘,对新兴的微信朋友圈网络、微博关注网络等涉及较少,并且缺乏高效的划分工具。为解决传统社团划分算法在大规模有向社交网络上无精确划分模拟模型,算法运行效率低,精度偏差大的问题。该文从构成社团结构最基础的三角形极大团展开数学推导,对网络节点的局部信息传递过程进行建模,并引入概率图有向矢量计算理论,对有向社交网络中具有较大信息传递增益的节点从数学基础创造性地构建了有向传递增益系数(Information Transfer Gain,ITG)。该文以此构建了新的有向社团结构划分效果的目标函数,提出了新型有向网络社团划分算法ITG,通过在模拟网络数据集和真实网络数据集上进行实验,验证了所提算法的精确性和新颖性,并优于Fast GN,OSLOM和Infomap等经典算法。
