基于空间自适应非凸正则项全变差相干斑噪声抑制

全变差正则化方法是相干斑噪声抑制研究的热点.非凸正则项能够更好地保持图像的边缘、纹理细节信息;空间自适应正则化参数可以根据像素点所在的区域,合理地控制噪声抑制程度,从而提高噪声抑制效果.本文结合非凸正则项和空间自适应正则化参数提出了一种新的全变差相干斑噪声抑制模型,并且给出了一种模型求解的数值算法.数值试验结果表明该模型能够获得更好的相干斑噪声抑制效果.     

一种改进的自适应中值滤波算法

针对椒盐噪声的特点,提出了一种改进的自适应中值滤波算法。该算法通过对小窗口内非噪声点的检测来决定是增大滤波窗口还是选择输出。新算法尽可能地减小了滤波窗口,使得图像细节得到更好的保持。数值试验结果表明,新算法能够在有效抑制噪声的同时更好地保持图像细节信息,尤其在高概率密度噪声条件（〉70%）下也能取得较好的结果,比传统自...     

自适应光学系统中的自适应控制算法研究

介绍和分析了一种在自适应光学系统中实用的自适应控制算法。与经典控制算法相比 ,自适应控制算法调整方便 ,综合考虑了大气湍流扰动和探测噪声等对系统控制效果的影响 ,控制参数能适应工作环境的变化 ,使系统达到最优控制状态。以 6 1单元自适应光学系统上实现的自适应控制算法为例 ,用经典控制理论的波德 (Bode)图、控制带宽等概念分析了这种算法的特点 ,并针对算法在实际系统应用中遇到的问题提出了解决的办法     

自适应光学系统的控制残余方差分析

分析自适应光学系统的控制残余方差与控制带宽的关系.对于存在时间延迟的自适应光学系统,推导了计算大气湍流控制残余方差的近似方式,并给出了有探测噪声情况下确定系统最优控制带宽的公式.以61单元自适应光学系统为例,讨论了实验自适应光学系统中控制带宽的优化选择问题.     

基于Markov随机场的自适应正则化三维显微图像复原

提出了基于马尔可夫随机场模型的正则化因子自适应调整三维显微图像复原算法,并用模拟序列样本和真实生物样本进行了实验.为了保持复原图像的边缘等细节信息,以Markov随机场模型作为图像的先验概率模型,对代价函数添加边缘约束惩罚项.其中,正则化因子在迭代过程中自适应地进行更新.实验结果表明此算法在对原始图像进行估计的同时,能够有效地保留图像的边缘等细节信息.而EM算法虽然能够有效地去除层间干扰,却丢失了大量的细节信息.     

自适应光学系统中的逢适应控制算法研究

介绍和分析了一种在自适应光学系统中实用的自适应控制算法。与经典控制算法相比，自适应控制算法调整方便，综合考虑了大气湍流扰动和探测噪声等对系统控制效果的影响，控制参数能适应工作环境的变化，使系统达到最优控制状态。以61单元自适应光学系统上实现的自适应控制算法为例，用经典控制理论的波德（Bode)图、控制带等概念分析了这种算法的特点，并针对算法在实际系统应用中遇到的问题提出了解决的办法。     

基于模型的无波前探测自适应光学系统

基于波前梯度的二阶矩和修正后的远场强度分布近似呈线性关系,设计了一种基于模型的无波前探测自适应光学系统快速闭环控制算法。使用61单元变形镜、CCD成像器件等建立了自适应光学系统仿真平台,并以不同湍流强度下的波前像差作为校正对象,分析了这种基于模型的无波前探测自适应光学系统的收敛速度、校正能力及对不同像差的适应性。结果表明,基于模型的无波前探测自适应光学系统在快速收敛的同时,能够获得接近波前校正器件的理想校正能力。N阶模式像差校正时,系统只需要进行N+1次远场光斑的测量。和现有的各种无波前探测自适应光学系统控制算法相比较,基于模型的无波前探测自适应光学系统所需的测量次数大大减少。     

基于加权块稀疏联合非凸低秩约束的高光谱图像去条带方法

针对高光谱图像（hyperspectral images, HSI）去条带易引起影像结构细节丢失问题,提出一种基于加权块稀疏（weighted block sparsity, WBS）正则化联合最小最大非凸惩罚（minimax concave penalty, MCP）约束的HSI去条带方法。本算法采用加权?_{2, 1}范数和MCP范数对条带稀疏结构和低秩约束,?₁范数对干净图像结构保持正则化约束,构建加权块稀疏和MCP约束的条带去除模型,采用交替方向乘子（alternating direction method of multipliers, ADMM）算法迭代求解对应模型,重建获得干净的HSI图像。实验结果表明,提出方法在实际HSI的平均等效视数从28.45提高到83.47,边缘保持指数较其他算法至少增加0.056,特别是对于非周期条带噪声,采用自适应权值更新稀疏水平,增强了组稀疏性,在保持影像边缘和加强区域平滑性方面性能更佳,去噪声效果更好。     

基于Contourlet变换的自适应SAR图像相干斑噪声抑制算法

为了保持高岛分辨率合成孔径雷达(SAR)图像中的纹理结构,提出了一种基于BivaShrink模型的Contourlet 域SAR图像相干斑噪声抑制算法.联合当前层和父层的Contourlet系数,通过计算局部方差一致性范数和区域能量比,自适应地确定方差估计区域的形状和大小.从而对原始图像方差进行最优估计.实验结果表明,算法在噪声的去除和结构信息等细节的保持上均不同程度的优于小波BivaShrink去噪算法和Contourlet 阈值去噪算法,主观效果和数值指标都有较好改进.     

自适应中值-加权均值混合滤波器

为了去除图像中混入的脉冲噪声和高斯噪声,提出了一种基于自适应中值滤波和自适应加权均值滤波的混合滤波方法。该方法先将图像分为若干区域,并对每个区域进行噪声检测以实现两类噪声的分离,然后再分别采用自适应中值滤波和自适应加权均值滤波将分离出的脉冲噪声和高斯噪声去除。对这种新方法进行了计算机模拟实验。结果表明:新方法较前人提及的三种混合滤波方法具有更优的滤波性能,在有效抑制混合噪声的同时能很好地保护图像中的细节,为消除图像中的混合噪声提供了一种有效的途径。     

点光源哈特曼最优阈值估计方法研究

针对夏克-哈特曼波前传感器探测系统中噪声随时间及空间变化频率较快的特点,为了准确估计系统的最优阈值,根据高斯光斑与噪声的分布特性,提出一种以滑动窗口内像素均值及图像信号的局部梯度作为参数,构造关于噪声权重函数的方法,由此获得子孔径阈值的最优估计值,并详细分析了算法的基本原理和实现过程.以典型处理方法获取的阈值与理论最优阈值的误差作为评价标准,仿真和实验结果表明本文提出的阈值估计方法在不同信噪比、不同光斑大小的条件下,均能取得优于典型阈值处理方法获得的结果,且与理论最优阈值的误差小于10%.     

采用L1/2稀疏约束的梅尔倒谱系数语音重建方法

提出了一种利用L_1/2稀疏约束从梅尔倒谱系数重建语音时域信号方法。从梅尔倒谱系数估计语音幅度谱是一个欠定问题,现有的方法均采用幅度谱最小均方误差估计或采用L1正则化进行幅度谱的稀疏约束。相比于L₁正则化模型,L_1/2的稀疏约束特性更强,为此,本文在从梅尔倒谱系数估计语音幅度谱时引入L_1/2正则化约束,并利用求解的稀疏幅度谱估计相位谱,最后利用估计的频谱重建时域语音信号。实验结果表明,与幅度谱最小均方误差法相比,本文算法所估计出的语音信号具有更高的语音质量;在噪声环境下进行语音重建实验,与L₁正则化幅度谱估计方法相比,本文算法重建的语音质量更好,表现出更好抗噪性。      

基于非凸正则化项的合成孔径雷达图像分割新算法

合成孔径雷达图像中乘性噪音的存在使合成孔径雷达图像分割变得非常困难.针对这一难题,本文以提高分割准确度,保护图像的几何结构边缘和提高算法的鲁棒性为目的,提出了一种适用于处理合成孔径雷达图像分割的新模型.新模型结合合成孔径雷达图像的区域和边缘信息,首先通过引入非凸的正则化项,定义了能量泛函;然后极小化能量泛函,建立了水平集函数演化的偏微分方程;最后对水平集演化方程的数值求解,实现了对合成孔径雷达图像感兴趣区域的分割.分别采用仿真图像和实测合成孔径雷达图像对新模型进行验证,结果表明,新模型对合成孔径雷达图像具有很强的边缘定位能力,能使目标区域分割更完整.     

基于非凸正则化项的合成孔径雷达图像分割新算法

合成孔径雷达图像中乘性噪音的存在使合成孔径雷达图像分割变得非常困难.针对这一难题,本文以提高分割准确度,保护图像的几何结构边缘和提高算法的鲁棒性为目的,提出了一种适用于处理合成孔径雷达图像分割的新模型.新模型结合合成孔径雷达图像的区域和边缘信息,首先通过引入非凸的正则化项,定义了能量泛函;然后极小化能量泛函,建立了水平集函数演化的偏微分方程;最后对水平集演化方程的数值求解,实现了对合成孔径雷达图像感兴趣区域的分割.分别采用仿真图像和实测合成孔径雷达图像对新模型进行验证,结果表明,新模型对合成孔径雷达图像具有很强的边缘定位能力,能使目标区域分割更完整.     

Blind image deconvolution with spatially adaptive total variation regularization

A blind deconvolution algorithm with spatially adaptive total variation regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish edges from flat areas. The proposed algorithm can effectively reduce the noise in flat regions as well as preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Comparative results on simulated and real degraded images are reported.     

An adaptive super-resolution method based on regional pixel information and ringing artifacts suppression

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. In this paper, a novel multi-frame image super-resolution algorithm is proposed based on regional pixel information and ringing artifacts suppression. Firstly, a new regularization term which adopts Regional Adaptive Weight Coefficients (RAWC) is produced to keep edges and flat regions. After detailed analysis, an iterative process is given for image reconstruction. Then an adaptive term according to the local variance of iterative correction image is designed to evaluate the ringing artifacts. Finally, the original iteration is updated by adding the restraint term for better visual effects and lower noise of reconstructive HR image. Thorough experimental results show the proposed algorithm is effective for SR reconstruction and ringing artifacts suppression.     

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ?₂ data fidelity term and ?₁ sparsity regularization term. Rather than manually setting the regularization parameter for the ?₁ term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression.     

Optimization for high precision Shack-Hartmann wavefront sensor

A comprehensive noise model about digital camera which is a main component of SHWFS is constructed, including the readout noise, the photon shot noise, the quantization noise and the response un-uniformity. Based on the noise model, the spot centroid errors caused by each kind of noise are analyzed, respectively. And then the synthetic error from all the noise is calculated. The result demonstrates that the limit of the spot centroid accuracy is 1% pixels. At last, the crossing error caused by the high order diffraction spots is analyzed. It is approximately proportional to the secondary spots number. So the structure of the microlens array must be optimized together with the digital camera when designing SHWFS.     

Single infrared image super-resolution combining non-local means with kernel regression

In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect.