Blind deconvolution: multiplicative iterative algorithm

A new algorithm has been developed for performing blind deconvolution on degraded images. The algorithm naturally preserves the nonnegative constraint on the iterative solutions of blind deconvolution and can produce a restored image of high resolution. Furthermore, benefiting from the multiplicative form, the algorithm is free from the instability of numerical computation. Results of applying the algorithm to simulated and real degraded images are reported.     

Blind image deconvolution using the Fields of Experts prior

In this paper, we present a method for single image blind deconvolution. To improve its ill-posedness, we formulate the problem under Bayesian probabilistic framework and use a prior named Fields of Experts (FoE) which is learnt from natural images to regularize the latent image. Furthermore, due to the sparse distribution of the point spread function (PSF), we adopt a Student-t prior to regularize it. An improved alternating minimization (AM) approach is proposed to solve the resulted optimization problem. Experiments on both synthetic and real world blurred images show that the proposed method can achieve results of high quality.     

Blind image deconvolution using the zero-lag slice of higher-order statistics

A novel algorithm for blind image deconvolution using the zero-lag slice (ZLS) of higher-order statistics only is presented. This method first estimates the point-spread function (PSF) using the ZLS of its third-order moment (TOM) and then uses it with one of the known classical image deconvolution methods. The proposed method has simple computations for PSF estimation because it solves a nonlinear problem by using an iterative method with fast convergence. In each iteration, one need only calculate the ZLS of the TOM and estimate the PSF using simple two-dimensional operations. Furthermore, the method presented achieves good results, since the ZLS estimate obtained from the degraded image exhibits high reliability. The good performance of the proposed algorithm is demonstrated by applying it to synthetic and real data sets.     

Blind image deconvolution using sparse and redundant representation

This work devotes to the image deconvolution problem that restores clear image from its blurred and noisy measurements with little prior about the blur. A deconvolution method based on sparse and redundant representation theory is developed in this paper. It firstly represents the blur and image over different redundant dictionaries and imposes sparsity constraint to their representation coefficients respectively, then alternately estimates them using an iterative algorithm employing optimization technique. Experimental results on astronomical images show that the proposed method can achieve as good performance as the method requiring a known blur, which demonstrates its effectiveness.     

基于垂直阵模态分解的低频水声信号盲解卷处理研究

研究了基于人工时反处理的水声信号盲解卷方法,并在此基础上提出了一种基于简正波模态分解的低频水声信号的盲解卷处理方法。该方法适用于浅海波导中垂直阵接收的远程低频水声信号的盲解卷处理。该方法首先从浅海中垂直阵接收的信号中提取(估计)出波导中传播的简正波模态函数信息,然后,根据估计的模态函数信息通过模态滤波来实现水声信道盲解卷处理。针对典型的浅海波导环境,进行了计算机仿真试验,结果表明:(1)远程低频条件下,模态分解方法可以从垂直阵接收的信号中提取出波导中有效传播的模态函数信息,因此这种方法解决了目前人工时反处理方法需要准确的模态函数先验信息的问题;(2)在一定带宽条件下,接收信号信噪比较低时,本文给出的这种基于模态滤波的盲解卷方法比人工时反处理具有更好的解卷性能。     

Blind data restoration with an extracted filter function

A method for performing blind deconvolutions on degraded images and data has been developed. The technique uses a power law relation applied to the Fourier transform of the degraded data to extract a filter function. This filter function closely resembles the point-spread function of the system and can be used to restore and enhance higher-frequency content. The process is noniterative and requires only that the point-spread function be space invariant and the transfer function be real. The algorithm has been validated by direct comparisons by use of a pseudoinverse filter with known transfer functions.     

Spectral restoration using semi-blind deconvolution method with detail-preserving regularization

Peak-details are often smoothed when deconvolution methods are used for spectral restoration. In order to preserve spectral details, detail-preserving regularization is devised and a semi-blind deconvolution method with the detail-preserving regularization (SBD-DP) is proposed. The cost function of SBD-DP is formulated and the numerical solution processes are deduced for restoring spectra and estimating parameter of blur kernel. The deconvolution results of simulated spectra demonstrate that the proposed SBD-DP can restore the spectrum effectively and has a merit on preserving peak details, as well as can estimate the parameter of blur kernel accurately. Then the deconvolution result of experimental Raman spectrum indicates the effectiveness of the proposed SBD-DP method on improving spectral resolution.     

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。     

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。     

Maximum a posteriori deconvolution of ultrasonic signals using multiple transducers

A new method for deconvolution of ultrasonic pulse-echo measurements employing multiple-transducer setup is proposed in the paper. An optimal way of estimating the material reflection sequence for a linear signal generation model using maximum a posteriori estimation is proposed. The method combines the measurements from a number of transducers covering different frequency bands yielding an optimal estimate of the reflection sequence. The main idea of this approach is to complement the information unavailable from one transducer in some frequency bands with the information from the other transducers. The method is based on the assumption that the measurements are performed using transducers with identical apertures and apodization, which are located exactly at the same position relative to the test object during the measurement. An error analysis presented in the paper proves that when the above assumptions are fulfilled, the proposed method, by utilizing more data for estimation, consistently yields more accurate reflection sequence estimates than the classical Wiener filter. Experimental evidence is presented using both simulated and real ultrasonic data as a verification of the correctness of the multiple-transducer model and the estimation scheme. An illustration of the advantages of the method is also given using real ultrasonic data.     

基于空间域共轭梯度法的盲目图像复原

针对图像退化的几种常见形式,在最小二乘法的基础上提出了一种基于共轭梯度法的空间域加权盲目图像复原方法。通过实验验证,该算法经过较少的迭代次数就可以实现运动模糊、离焦模糊图像的复原,并取得了较为满意的复原结果。     

A new technique for the identification of ultrasonic flaw signals using deconvolution

The identification of ultrasonic flaw signals is a difficult task in the angle beam ultrasonic testing of welded joints due to the presence of non-relevant signals from the geometric reflectors such as weld-roots and counter-bores. This paper describes a new approach to identify ultrasonic flaw signals in such a problematic situation. A similarity function is defined as the deconvolution of a target signal by a reference signal. The similarity functions for the same type of flaws/references are symmetric bandlimited impulse-like patterns with larger amplitudes while those for different types of flaws/references are asymmetrical broad patterns with relatively smaller amplitudes. Therefore, ultrasonic signals could be identified by the pattern of the similarity function. In the initial experiments, the proposed technique showed great potential for identifying ultrasonic flaw signals in the inspection of weld joints.     

Maximum a posteriori deconvolution of sparse ultrasonic signals using genetic optimization

Deconvolution of sparse spike sequences has received much attention in the field of seismic exploration. In certain situations in ultrasonic non-destructive testing (NDT) of materials, similar conditions as those found in seismic exploration occur. One example is the problem of detecting disbonds in layered aluminum structures. The reflection sequence convolved with the impulse response of the transducer results in masking closely spaced reflections. Deconvolution of these signals may reveal the reflection sequence and thus make the interpretation easier. In this paper we use the Bernoulli-Gaussian (BG) distribution for modeling the signal generation. This relatively simple model allows maximum a posteriori (MAP) estimation of the reflection sequence. A derivation of the MAP criterion is given for clarity. We propose a genetic algorithm for optimizing the MAP criterion. The genetic algorithm approach is motivated by the fact that the criterion is non-convex, implying that the criterion may have more than one local minimum point. The probability of obtaining the global optimal solution is increased by using the proposed genetic algorithm. One of the key features in genetic algorithms, the so-called cross-over operator, has been modified and adapted to the structure of the BG deconvolution problem to improve the efficiency of the search. The algorithm is tested on simulated data using the probability of detection (PD) and probability of false alarm (PFA) as evaluation criteria. The algorithm is also tested on real ultrasonic data from a layered aluminum structure. The results show considerable improvements in the possibility of interpreting the signals.     

卷积混迭语音信号的联合块对角化盲分离方法

针对语音信号的卷积混迭模型,利用不同语音信号之间的近似独立和短时平稳特性,提出一种基于信号二阶统计量的联合块对角化方法,解决超定卷积盲分离问题。该方法采用非对角线上各子矩阵 F -范数的平方和作为联合块对角化性能的评判准则,将原四次代价函数转化为一组较为简单的二次子代价函数,每一子代价函数用于估计酉混迭矩阵的一个子矩阵。依次最小化各子函数,迭代搜索代价函数最小点,得到混迭矩阵的估计。理论分析及实验结果表明,所提方法不仅能够达到与类Jacobi经典方法同样好的分离效果,并且具有更低的计算复杂度、更快的收敛速度和对传输信道阶数、迭代初始值不敏感的特点。     

基于Poisson-Markov分布最大后验概率的多通道超分辨率盲复原算法

在Poisson和Markov随机场分布假设基础上,改进了存在噪声时多通道互限制原则,提出了一种基于最大后验概率判据的多通道图像盲复原算法,可在无需已知通道内离散点扩散函数性质、类型和具体分布等先验知识的情况下,在图像幅度限制约束空间和离散点扩散函数能量恒定约束空间内,通过对图像和离散点扩散函数的交替迭代,使两者收敛到全局最优解,最终复原出超分辨率图像.对计算机模拟和实际存在微平移、微离焦情况的图像处理表明：算法在不同信噪比水平和非命中PSF尺寸下具有良好的图像复原效果和稳定性. 关键词： 泊松-马尔科夫分布 最大后验概率 多通道 盲复原     

Reconstruction of chaotic signals using symbolic data

We discuss the reconstruction of dynamical systems from noisy time-series. In particular, we consider the use of the symbol statistics (coarse-grained signal data) as the target for reconstruction. The statistics of symbol sequences is relatively insensitive to moderate amounts of measurement noise (σ(noise)/σ(signal) ≈ 10–20%), while larger amounts produce a substantial bias. We show that it is possible to produce robust reconstructions even when σ(noise)/σ(signal) ≈ O(1). Our study shows that even at such high noise levels the procedure is convergent: i.e. the accuracy of parameter estimates is limited only by the amount of data and computer time available.     

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.     

斜坡海底波导中随机声源的信道盲解卷积

该文研究了在斜坡海底环境中,通过基于射线的盲解卷积(RBD)方法,利用随机声源发出的信号来估计信道响应(CIR)的有效性,并讨论了不同信噪比和海底坡度情况下,RBD估计到的CIR与CIR理论值的相关性,对2016年美国圣巴巴拉海峡实验进行了仿真验证。RBD方法通过宽带波束形成估计随机声源的相位,以得到声源和接收阵之间的CIR。仿真结果表明,斜坡海底情况下RBD方法仍能较为准确地估计信道的CIR,噪声和海底坡度的变化均会对RBD估计到的CIR的精确度产生一定的影响。在具有一定粗糙度的斜坡海底波导或存在海底山丘的波导中,RBD仍然可以对CIR进行有效估计。     

基于总变分的湍流退化噪声图像盲反卷积复原

根据总变分的噪声抑制特性和大气湍流成像过程,建立了基于总变分的大气湍流噪声图像多帧盲反卷积复原最小化模型,以基于共轭梯度数值优化方法的交替迭代算法求解,复原出了观测目标的清晰图像。在计算机上模拟了湍流退化和噪声污染图像。实验结果表明,该复原算法能有效地克服大气湍流和噪声的影响,可复原出清晰的原始目标图像。