Spectral semi-blind deconvolution with hybrid regularization

A spectral semi-blind deconvolution with hybrid regularization (SBD-HR) is proposed to recover the spectrum and to estimate the parameter of the point spread function (PSF) adaptively. Firstly, a weighted Tikhonov regularization term about the spectrum is presented to preserve the details of spectrum. Then the regularization term about the PSF is modeled as L1-norm instead of L2-norm to enhance the stability of kernel estimation. The numerical solution processes for recovering the spectrum and for estimating the parameter of the PSF are deduced. Simulation results of infrared spectrum deconvolution demonstrate that the proposed method can recover the spectrum better from the degraded spectrum and estimate the parameter of the PSF accurately.     

Spectral semi-blind deconvolution with least trimmed squares regularization

A spectral semi-blind deconvolution with least trimmed squares regularization (SBD-LTS) is proposed to improve spectral resolution. Firstly, the regularization term about the spectrum data is modeled as the form of least trimmed squares, which can help to preserve the peak details better. Then the regularization term about the PSF is modeled as L1-norm to enhance the stability of kernel estimation. The cost function of SBD-LTS is formulated and the numerical solution processes are deduced for deconvolving the spectra and estimating the PSF. The deconvolution results of simulated infrared spectra demonstrate that the proposed SBD-LTS can recover the spectrum effectively and estimate the PSF accurately, as well as has a merit on preserving the details, especially in the case of noise. The deconvolution result of experimental Raman spectrum indicates that SBD-LTS can resolve the spectrum and improve the resolution effectively.     

Blind restoration of real turbulence-degraded image with complicated backgrounds using anisotropic regularization

This paper proposes a novel blind image restoration method based on estimating the point-spread functions by using two real turbulence-degraded images as input. The non-negative constraint and the spatial correlation are transformed mathematically into the penalty terms and added to the objective function. An anisotropic and nonlinear regularization function is proposed to adequately punish the differences of the point spread functions (PSFs) in the process of optimization estimation. Some definitions of weighted second-order differences are given and a fast method to construct the matrix of second-order weighted gradient operator is derived. The PSF values can be quickly estimated. With the estimated PSFs, the true images can be recovered by non-blind restoration methods. Experiment results for the restoration of real turbulence-degraded images with complicated backgrounds support the effectiveness of this proposed method.     

双树复数小波变换的视网膜图像半盲解卷积复原

针对导致自适应光学视网膜图像降质退化的原因,提出了一种结合双树复数小波变换(DT-CWT)和图像半盲解卷积复原算法的方法。首先,对经过自适应光学实时校正技术得到的视网膜图像进行DT-CWT分解,得到低频和高频部分对应的图像。将自适应光学成像系统中残余像差重建的光学传递函数作为图像复原模型的初始估计点扩散函数(PSF),并对低频部分图像进行条件约束的迭代半盲解卷积复原;对高频部分的图像进行去噪处理。最后,将处理后的高频和低频部分图像进行双树复数小波逆变换,获得复原图像。实验和结果表明：由该方法处理的视网膜细胞图像质量得到明显提高,图像客观质量评价参数相对于原始图像提高了5倍多;在视网膜细胞的空间频率范围内(70~90()-1),复原图像功率谱平均值提高了5倍左右,有助于对视网膜细胞的高分辨率观察。     

双树复数小波变换的视网膜图像半盲解卷积复原

针对导致自适应光学视网膜图像降质退化的原因,提出了一种结合双树复数小波变换(DTCWT)和图像半盲解卷积复原算法的方法。首先,对经过自适应光学实时校正技术得到的视网膜图像进行DT-CWT分解,得到低频和高频部分应的图像。将自适应光学成像系统中残余像差重建的光学传递函数作为图像复原模型的初始估计点扩散函数(PSF),并对低频部分图像进行条件约束的迭代半盲解卷积复原;对高频部分的图像进行去噪处理。最后,将处理后的高频和低频部分图像进行双树复数小波逆变换,获得复原图像。实验和结果表明:由该方法处理的视网膜细胞图像质量得到明显提高,图像客观质量评价参数相对于原始图像提高了5倍多;在视网膜细胞的空间频率范围内(70~90(°)~1),复原图像功率谱平均值提高了5倍左右,有助于对视网膜细胞的高分辨率观察。     

模糊条码图像的正则化复原算法

由条码扫描仪获得条码图像的过程可以用理想条码信号与扫描仪光学系统点扩散函数的卷积模型来描述。反卷积是消除由光学系统点扩散带来的模糊现象的最好办法。为克服反卷积的病态问题,研究了反卷积的正则化方法;针对条码信号的特点,构建了适合于条码信号复原的惩罚项,提出了条码信号的正则化复原算法及其适合于计算机运算的迭代算法。通过实验研究了算法在不同情况下的抗干扰能力。实验结果表明,正则化条码信号复原算法在消除系统点扩散函数的影响的同时能够很好地抑制噪声。     

波前解卷积方法中的高频噪声抑制

介绍了一种波前解卷积中噪声抑制规整化的新方法,并将此方法应用于室内模拟点源实验中。该方法通过在图像复原算法中增加针对图像高频部分的限制条件来抑制高频噪声,以达到对图像复原问题病态特性的规整化。实验结果表明：该规整化方法可以有效地抑制解卷积过程中高频噪声的影响,恢复出达到理论衍射极限分辨率的图像。对于噪声水平较高的降质图像,通过这种解卷积方法可以有效地提高信噪比。同维纳逆滤波方法相比,该方法可以在有效抑制导致病态的高频噪声的基础上充分保持图像的低频;与基于贝叶斯估计的近视解卷积算法相比,该方法不需要知道噪声水平或噪声类型等先验知识,只是从噪声本质出发,通过抑制降质图像高频部分,有效地解决了病态特性问题。     

空间变化PSF非盲去卷积图像复原法综述

传统的图像复原一般认为点扩散函数(PSF)是空间不变的,实际光学系统由于受到像差等因素的影响,并非严格的线性空间不变系统,基于空间变化PSF的非盲去卷积图像复原法逐渐体现其优越性。空间变化PSF的非盲去卷积图像复原法先准确估计图像空间变化的PSF,再利用非盲去卷积算法对图像进行复原,有利于恢复出高质量图像。本文从算法的角度综述了近几年提出的基于空间变化PSF的非盲去卷积图像复原方法,并对比了基于强边缘预测估计PSF的非盲去卷积法、基于模糊噪声图像对PSF估计非盲去卷积法等算法的优缺点,各算法分别在PSF估计精确度、振铃效应抑制效果、适用范围等方面体现出各自的优劣。空间变化PSF的非盲去卷积图像复原法的研究,有利于推进图像复原技术向更高水平发展,使光学系统往轻小型化方向发展,从而在多个科学领域发挥其重要作用。     

改进的固定点图像复原算法（英文）

研究了周期边界条件下,Tikhonov正则化的固定点算法,提出了变化正则化参数的方法。首先对正则化参数取较大值,抑制复原图像中的噪声,通过得出的收敛结果来修正初始梯度;然后对正则化参数取较小值,以增强复原图像中的细节。实验结果表明,与当前求解L1范数正则化函数和全变分正则化函数的流行算法比较,本文算法对于运动模糊与高斯模糊图像的复原效果更佳。     

自适应光学图像非对称图像迭代盲复原算法

为了提高自适应光学图像复原效果,提出了一种新的多重约束非对称图像迭代盲解卷积算法。首先,在点扩散函数（PSF）频率域引入带宽有限约束来提高迭代盲解卷积算法的可靠性;然后,在PSF空间域引入支持域动态更新的思想以加快迭代盲解卷积算法收敛速度;最后,自动计算迭代盲解卷积算法的非对称因子以提高算法的自适应性。模拟实验结果表明,与RL-IBD算法比较,新算法迭代次数减少22.4%、峰值信噪比提高10.18 dB。在FK5-857和某双星的自适应光学图像复原实验中,也取得很好的复原效果。     

自适应光学图像非对称图像迭代盲复原算法

 为了提高自适应光学图像复原效果,提出了一种新的多重约束非对称图像迭代盲解卷积算法。首先,在点扩散函数（PSF）频率域引入带宽有限约束来提高迭代盲解卷积算法的可靠性;然后,在PSF空间域引入支持域动态更新的思想以加快迭代盲解卷积算法收敛速度;最后,自动计算迭代盲解卷积算法的非对称因子以提高算法的自适应性。模拟实验结果表明,与RL-IBD算法比较,新算法迭代次数减少22.4%、峰值信噪比提高10.18 dB。在FK5-857和某双星的自适应光学图像复原实验中,也取得很好的复原效果。     

改进的盲解卷积技术在远距离激光光斑图像复原中的应用

为了提高外场实验中远距离测量激光光斑位置的精度,提出利用盲解卷积技术对光斑图像进行事后复原来削弱大气湍流对光斑成像的影响。首先,介绍了经典盲解卷积算法,分析了其不足之处,并提出了一种改进的盲解卷积算法。为了提高目标函数的收敛性和收敛速度,在TV(Total Variation)目标函数加入惩罚项,并对交替迭代法进行改进。然后,用数学方法证明了改进的盲解卷积算法的收敛性。最后,进行了仿真实验。与传统算法相比,用改进算法恢复的图像信噪比至少提升了15%。文中给出了外场试验图像的实际复原效果。     

Multiframe blind image deconvolution with split Bregman method

In the paper, a multiframe blind image deconvolution method based on total variation and framelet regularizer is proposed. An adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problems. In each iteration, four sub-problems need to be solved, one of which can be very efficiently and easily solved via fast Fourier transform implementation or closed form solution. Both simulated noisy and blurred frames and real degraded frames are used to verify the effectiveness of the proposed method. Comparative experimental results show that the proposed method can efficiently remove the blur and noises and restore high quality sharp image.     

Iterative Wiener deconvolution based method for phase retrieval from noisy intensities

To retrieve the phase from the noisy measured intensities in the diffraction planes, an iterative Wiener deconvolution based method is proposed. With the same iterative scheme as the iterative angular spectrum method (IAS), the propagation of the optical wave function between the input plane and the diffraction planes is calculated by Wiener deconvolution in this method. The angular spectrum convolution kernel used in the iterative angular spectrum method is incorporated into the Wiener filter. The simulation experiments show that the proposed method can reduce the impact of the noise on the retrieved phase and performed better than the pre-denoising method. Furthermore, the proposed method exhibits great advantage compared to IAS for retrieving the complicated phase distribution from two measured intensities.     

遗传算法结合正则化方法反演海洋大气波导

针对正则化方法在解决实际反演问题时既能克服问题的不适定性又可以很大程度上抑制噪声和误差的传播, 本文提出了利用遗传算法结合正则化方法的新算法, 在遗传算法适应度函数中引入正则化项来反演波导参数; 然后对算法进行仿真试验, 结果表明新算法与传统遗传算法相比具有较高的反演精度, 并指出当噪声误差小于10%时, 算法具有较强的“去噪”性能; 最后利用机载雷达在Wallops岛探测的海表面处局部回波资料进行反演试验, 将反演结果与实测大气折射率廓线进行比较, 说明该算法的有效性. 新方法为海洋大气波导反演研究提供了一种新思路.     

Gradient-Based Blind Deconvolution with Phase Spectral Constraints

This paper proposes a new blind deconvolution method with additional phase spectral constraints for a blurred image. A degradation of an original image is mathematically modeled by a convolution of an original image and a point-spread function (PSF). The proposed method consists of the following three steps: (i) projection onto a complex set satisfying the phase spectral constraint in a frequency space; (ii) minimization of a cost function preserving the constrained phase spectra; and (iii) projection onto an image space satisfying nonnegative and support constraints. This method restores both the original image and the PSF with high accuracy. The effectiveness of the proposed method is verified by applying it to some blind deconvolution problems for digital images, and the experimental results show that the performance is superior to the conventional blind deconvolution methods.     

基于DCGAN的拉曼光谱样本扩充及应用研究

拉曼光谱检测方法依赖于化学计量学算法,深度学习是当下最炙手可热的方向,可应用于拉曼光谱进行建模.但是深度学习需要大样本进行训练,而拉曼光谱采集受制于器材和人力成本,获取大批量的样本需要更大成本,且易受荧光等因素干扰,这些问题都制约了将深度学习应用于拉曼光谱.针对以上问题,通过引入深度卷积生成对抗网络(DCGAN)提取拉...     

复原红外光谱的希尔伯特—史密特方法

对几种典型红外光谱仪,利用希尔伯特-史密特方法解积分方程能得到高分辨复原光谱,并能有效地抑制噪声影响。把较大波数范:围分成几个小区域,适当选取参数,可大大减少计算点数(～41)。对非对称核的积分方程,用修正的希尔伯-史密特方法能进一步减少计算时间和容量。该方法还可以用于增大傅里叶光谱仪的光源立体角。     

利用变分结合正则化方法对高度计风速资料调整海面风场的研究

利用变分结合正则化方法,对高度计风速资料在无辐散和有辐散两种情形下的背景风场进行调整,同时进行了数值试验.试验表明:高度计风速对背景风场的调整有积极作用,特别是高度计沿轨区域风场调整效果更为明显.对高度计后向散射截面进行了敏感性试验,当后向散射截面存在随机扰动时,利用高度计风速调整海面风场具有较强的抗噪性.最后进行实例试验,结果表明该方法是切实可行的.     

一种光谱识别的新方法

提出了一种对光谱信号识别的新方法。针对光谱信号的特征 ,我们设计了基于径向基函数神经网络组成的统计混合模型 ,并构造了识别系统的代价函数。通过优化系统的代价函数 ,导出了类EM算法去估计混合模型的参数 ,从而构建对光谱特征识别的识别器。利用实际的拉曼光谱 ,对本文所提出的估计模型参数的算法与建立的光谱识别器进行了检验。我们还讨论了利用特征波长与相应的光谱强度 ,以及利用主分量分析组成输入特征矢量 ,及其这些输入特征矢量对光谱识别器应用的效果。实验结果表明 ,所提出的算法可以有效地估计模型参数 ,其建立的光谱识别模型具有较高的识别准确率。所提出的对光谱信号识别的方法通用性强 ,因此具有较为广阔的应用前景。