Experimental validation of Fourier-transform wave-front reconstruction at the Palomar Observatory

Wave-front reconstruction with use of the Fourier transform has been validated through theory and simulation. This method provides a dramatic reduction in computational costs for large adaptive (AO) systems. Because such a reconstructor can be expressed as a matrix, it can be used as an alternative in a matrix-based AO control system. This was done with the Palomar Observatory AO system on the 200-in. Hale telescope. Results of these tests indicate that Fourier-transform wave-front reconstruction works in a real system. For both bright and dim stars, a Hudgin-geometry Fourier-transform method produced performance comparable to that of the Palomar Adaptive Optics least squares. The Fried-geometry method had a noticeable Strehl ratio performance degradation of 0.043 in the K band (165-nm rms wave-front error added in quadrature) on a dim star.     

字典学习稀疏表示的高光谱图像异常检测

针对稀疏表示高光谱检测算法性能受背景字典影响较大的问题,充分利用高光谱图像空间信息和光谱主成分信息,提出了一种基于字典学习的稀疏表示异常检测算法。首先利用主成分分析提取高光谱数据的主特征,建立目标主成分空间,并证明了在主成分空间进行字典学习稀疏重构的可行性;然后在主成分空间内构造基于K-SVD算法的训练字典,改善了背景字典性能;采用正交匹配算法重构主成分分量,利用主成分分析反变换得到待检测像元重构光谱,增强了高光谱图像的局部异常特性;最后,基于重构误差异常特性实现高光谱图像异常检测。仿真结果证明了该方法的有效性。     

Application of Robust Incomplete Cholesky Factorizations Preconditioned CG Algorithms for FEM Analysis of Millimeter Wave Filters

The Incomplete Cholesky factorizations preconditioning scheme is applied to the conjugate gradient (CG) method for solving a large system of linear equations resulting from finite element method (FEM) analysis of millimeter wave filters. As is well known, the convergence of CG method deteriorates with increasing EM wave number and in millimeter wave band the eigen-values of A are more and more scattered between both the right and the left half-plane. The efficient implementation of this preconditioned CG (PCG) algorithm is described in details for Complex coefficient matrix. With incomplete factorization preconditioning scheme in the conjugate gradient algorithm, this PCG approach can reach convergence in 20 times CPU time shorter than CG for several typical millimeter wave structures.     

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

Adaptive optics wave function reconstruction and phase unwrapping when branch points are present

An algorithm is presented for reconstruction of adaptive optics wavefront sensor data which produces results that contain the part of the wavefront distortion associated with the hidden phase as well as the scalar phase part that is captured by an ordinary least-mean-square-error reconstructor. The algorithm uses a multigrid formulation and multiplication of complex phasors representing the measured phase differences to reconstruct the distorted wave function. The algorithm is formulated to work with Hudgin-geometry data, but in the appendix a variant of this algorithm is described that allows operation with Fried-geometry data. Also an algorithm is presented that generates a phase function from the reconstructed wave function, a phase function that has its branch cuts placed so that the 2π discontinuities of the branch cut occur where the optical intensity is much lower than the average intensity. The reconstructor algorithm is formulated as a noise-variance-weighted reconstructor. It is found that the algorithm's noise gain is only slightly greater than that of a noise-variance-weighted least-mean-square-error reconstructor so long as the noise variance for the input phase difference measurement data is less than about 0.25 rad².     

一种区域波前重构的快速算法

本文提出了一种分块进行波前重构的快速算法。它具有所需存贮量小、噪声传递系数小及数值稳定性好等特点。定性分析与计算机模拟结果表明,由于算法本身带来新的重构误差,它的重构精度与通常算法基本相同。     

基于瀑布型多重网格加速的复指数波前复原算法

激光在大气中传输时,由于强湍流或长传输距离的影响,畸变波前中出现由相位起点组成的不连续相位,现有波前复原算法不能有效复原不连续相位,使得自适应光学系统校正效果下降甚至失效.本文分析了最小二乘波前复原算法不能复原相位奇点的原因,提出了基于瀑布型多重网格加速的复指数波前复原算法,给出了复指数波前复原算法中迭代计算、降采样、插值计算的实现方式.研究了该方法对不连续相位和随机连续相位的复原能力,数值分析了采用复指数波前复原算法的自适应光学系统对大气湍流像差的校正效果.仿真结果表明,同等复原精度下,相比直接迭代过程,该方法所需浮点乘数目减少了近2个数量级,且随着夏克-哈特曼波前传感器子孔径数目增加,其在计算量上的优势更加明显. Rytov方差较大时,相比直接斜率法,自适应光学系统采用复指数波前复原算法后校正光束Strehl比提升1倍.     

Sparse representation-based image restoration via nonlocal supervised coding

Sparse representation (SR) and nonlocal technique (NLT) have shown great potential in low-level image processing. However, due to the degradation of the observed image, SR and NLT may not be accurate enough to obtain a faithful restoration results when they are used independently. To improve the performance, in this paper, a nonlocal supervised coding strategy-based NLT for image restoration is proposed. The novel method has three main contributions. First, to exploit the useful nonlocal patches, a nonnegative sparse representation is introduced, whose coefficients can be utilized as the supervised weights among patches. Second, a novel objective function is proposed, which integrated the supervised weights learning and the nonlocal sparse coding to guarantee a more promising solution. Finally, to make the minimization tractable and convergence, a numerical scheme based on iterative shrinkage thresholding is developed to solve the above underdetermined inverse problem. The extensive experiments validate the effectiveness of the proposed method.     

快速傅里叶算法在哈特曼夏克传感器波前重构算法中的应用

提出了一种应用快速傅里叶变换算法提高哈特曼－夏克波前传感器波前重构实时性的快速算法，在根据波前斜率值应用最小二乘法估计波前相位的过程中，应用快速傅里叶变换算法进行方程的对角化和相位值的解耗，算法精站度，稳定，空间分辨率越高，算法实时性的优越性就越显著。     

合成孔径压缩感知超声成像中的高效能稀疏字典设计

针对目前常见的稀疏字典缺乏针对性,在合成孔径医学超声成像中的应用效果不佳,难以在低压缩率下保证重构图像质量的问题,本文设计了一种高效能的稀疏字典。根据超声回波信号是由发射脉冲信号经过不同延时衰减后叠加的特点,利用发射脉冲作为基函数构造稀疏字典,回波信号在该稀疏字典确定的变换域中具备很好的稀疏性,理论上能使其稀疏表示系数的稀疏度等于超声阵元接收到的反射回波数。通过FieldⅡ对简单点目标和复杂目标的仿真结果表明:在相同的重构算法和压缩率下该稀疏字典重构的平均绝对误差明显小于常见的稀疏字典,其值仅为DWT的几分之一,DFT和DCT的几十分之一,能让回波信号以更低的压缩率实现相同的恢复效果。本文最后使用体模的实际采集数据对算法的实际效果进行检测,实验结果也与仿真结果基本一致。基于该稀疏字典的压缩感知算法可以进一步减少合成孔径成像所需存储的数据量、降低系统的复杂度。      

一种八谱段滤光片成像系统设计

针对多光谱滤光片阵列成像采样率低,原始(Raw)数据稀疏所导致的重建图像模糊,高频信息丢失等问题,提出了一种新八谱段滤光片阵列分布方案,利用基于邻域梯度延伸方法对光谱Raw图像进行重建.首先基于二叉树生成法,在重复排列的4×4阵列中设计了一种等空间概率比的八谱段滤光片分布方案;然后针对传感器直接获取的稀疏Raw图像,计算各谱段采样点的梯度信息,在保持图像结构特征和纹理信息的基础上,利用邻域采样点的像素值和梯度值对未采样点进行重建,从而获得完整的光谱图像信息;最后,基于已重建的八谱段光谱图像,采用伪逆矩阵法重构各像素位置的31波段光谱值.结果表明,相对于主流图像重建方法,本文算法提高了重建八谱段光谱图像的峰值信噪比、复合峰值信噪比,降低了光谱均方差,更好地保留了图像的纹理和边缘,有效降低了多光谱滤光片阵列成像中的颜色伪影和图像模糊等现象.     

Sparse Bayesian reconstruction method for multispectral bioluminescence tomography

We present a sparse Bayesian reconstruction method based on multiple types of a priori information for multispectral bioluminescence tomography （BLT）. In the Bayesian approach, five kinds of a priori information are incorporated, reducing the ill-posedness of BLT. Specifically, source sparsity characteristic is considered to promote reconstruction results. Considering the computational burden in the multispectral case, a series of strategies is adopted to improve computational efficiency, such as optimal permissible source region strategy and node model of the finite element method. The performance of the proposed algorithm is validated by a heterogeneous three-dimensional （3D） micron scale computed tomography atlas and a mouse-shaped phantom. Reconstructed results demonstrate the feasibility and effectiveness of the proposed algorithm.     

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

Multi-focus-image-fusion is a crucial embranchment of image processing. Many methods have been developed from different perspectives to solve this problem. Among them, the sparse representation (SR)-based and convolutional neural network (CNN)-based fusion methods have been widely used. Fusing the source image patches, the SR-based model is essentially a local method with a nonlinear fusion rule. On the other hand, the direct mapping between the source images follows the decision map which is learned via CNN. The fusion is a global one with a linear fusion rule. Combining the advantages of the above two methods, a novel fusion method that applies CNN to assist SR is proposed for the purpose of gaining a fused image with more precise and abundant information. In the proposed method, source image patches were fused based on SR and the new weight obtained by CNN. Experimental results demonstrate that the proposed method clearly outperforms existing state-of-the-art methods in addition to SR and CNN in terms of both visual perception and objective evaluation metrics, and the computational complexity is greatly reduced. Experimental results demonstrate that the proposed method not only clearly outperforms the SR and CNN methods in terms of visual perception and objective evaluation indicators, but is also significantly better than other state-of-the-art methods since our computational complexity is greatly reduced.     

High-speed horizontal-path atmospheric turbulence correction with a large-actuator-number microelectromechanical system spatial light modulator in an interferometric phase-conjugation engine

Results of atmospheric propagation for a high-speed, large-actuator-number adaptive optics system are presented. The system uses a microelectromechanical system- (MEMS-) based spatial light modulator correction device with 1024 actuators. Tests over a 1.35-km path achieved correction speeds in excess of 800 Hz and Strehl ratios close to 0.5. The wave-front sensor was based on a quadrature interferometer that directly measures phase. This technique does not require global wave-front reconstruction, making it relatively insensitive to scintillation and phase residues. The results demonstrate the potential of large-actuator-number MEMS-based spatial light modulators to replace conventional deformable mirrors.     

An Adaptive Denoising Algorithm for Noisy Chaotic Signals Based on Local Sparse Representation

An adaptive denoising algorithm based on local sparse representation （local SR） is proposed. The basic idea is applying SR locally to clusters of signals embedded in a high-dimensional space of delayed coordinates. The clusters of signals are represented by the sparse linear combinations of atoms depending on the nature of the signal. The algorithm is applied to noisy chaotic signals denoising for testing its performance. In comparison with recently reported leading alternative denoising algorithms such as kernel principle component analysis （Kernel PCA）, local independent component analysis （local ICA）, local PCA, and wavelet shrinkage （WS）, the proposed algorithm is more efficient.     

Iterative wave-front reconstruction for open-loop metrology applications: Shack-Hartmann and shearing interferometer wave-front sensing using Fourier or Multigrid reconstructors

Numerous techniques exist to reconstruct the wave-front in Shack-Hartmann and shearing interferometer wave-front sensors. Two of these techniques involve solving the least-squares wave-front reconstruction via Fourier transform or Multigrid methods. These two techniques operate on a square grid and are sensitive to both nonlinearities and to the methods used to extend the gradients beyond the aperture to the edges of the square grid. Both of these sensitivities can lead to large residual wave-front errors when reconstructing large aberrations. In this article we introduce an iterative computational loop in the reconstruction process which is designed to improve the reconstruction variances introduced by these parameters. This technique allows for accurate reconstruction of large phases which can arise in applications such as open-loop compensation of atmospheric turbulence, aberrometers and metrology. In this article, it is demonstrated that large aberrations can be accurately reconstructed utilizing this iterative method with Fourier or Multigrid reconstructors.     

Experimental validation of single-iteration multigrid wavefront reconstruction at the Palomar Observatory

Single-iteration multigrid (SIMG) wavefront reconstruction schemes were implemented and validated on the adaptive optics system at the Hale 5.1 m telescope at the Palomar Observatory. Results indicate that even the simplest such method produces a performance indistinguishable from that of the standard least-squares reconstructor for both bright and dim guide stars. SIMG provides a dramatic reduction in computational cost when compared to vector-matrix multiplication and can be implemented in parallel, making it the obvious choice for reconstruction in future large-scale adaptive optics systems.     

基于高斯混合尺度模型的压缩传感图像重构

在图像处理领域中,压缩传感重构是稀疏表示下的最重要的病态反问题之一。压缩传感图像重构利用图像可稀疏表示的先验知识,从比奈奎斯特采样率低得多的随机投影观测值中重构原始图像。为了克服传统的压缩传感算法中收敛速度慢和未利用变换系数的邻域统计特性的缺点,提出了基于高斯混合尺度模型的压缩传感图像重构算法,证明了独立的高斯混合尺度分布作为压缩传感重构的稀疏先验知识的可行性,结合全变差调整进一步提高算法的性能。实验结果表明,该算法有效地提高了重构图像的主观视觉效果和峰值信噪比,加快了压缩传感图像重构算法的收敛速度。     

基于联合正则化及压缩传感的MRI图像重构

基于压缩传感的MRI图像重构利用图像稀疏的先验知识能从很少的投影值重构原图像。目前MRI重构算法只利用MRI图像稀疏性表示或只利用基于其局部光滑性的先验知识,重构效果不理想。针对此问题,结合两种先验知识,提出一种基于联合正则化及压缩传感的MRI图像重构方法。利用块坐标下降法将求解联合正则化问题转化为交替求解二次凸优化、稀疏正则化和全变差正则化三个简单的优化问题。并提出分别采用共轭梯度法、二元自适应收缩法以及梯度下降法对以上优化问题求解。实验结果表明,该算法重构效果比现有算法有明显地提高。     

基于稀疏优化的计算机断层成像图像不完全角度重建综述

计算机断层成像(computed tomography,CT)技术在医学和工业无损检测中都具有非常广泛的应用,CT重建算法是其中的核心,而不完全角度重建问题则是实际应用中重建算法研究领域的一个热点和难点问题.近年来,随着稀疏优化理论与算法的飞速发展,基于稀疏优化的重建算法已经在不完全角度重建问题中得到了较广泛的应用,且表现出了良好的精度与速度性能.本文首先对稀疏优化的基本理论结论与常用算法进行了介绍;而后对稀疏优化理论在CT图像不完全角度重建中的应用进行归纳,分类介绍了其主要研究成果及稀疏优化所发挥的作用;最后对基于稀疏优化的不完全角度重建研究进行了展望.