Comparative study on several blind deconvolution algorithms applied to underwater image restoration

Although the use of blind deconvolution of image restoration is a widely known concept, only few reports have discussed in detail its application to solving problem of restoration of underwater range-gated laser images. A comparative study of underwater image restoration using the Richardson-Lucy algorithm, the least-squares algorithm, and the multiplicative iterative algorithm for blind deconvolution is presented. All the deconvolution approaches use denoised underwater images and Wells’ small angle approximation theory of derived point spread function as the initial object and degradation guess, respectively. Owing the underwater no-reference imaging environment, image quality judgment based on the blur metric method is incorporated in our comparison to determine the appropriate deconvolution iteration number for each algorithm, which objectively evaluates the image restoration results. The performance of the three algorithms applied to underwater image restoration is discussed and reported.     

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。     

天文图像多帧盲反卷积收敛性的增强方法

天文图像多帧盲反卷积的收敛性受到初始目标、约束条件和光子噪声等因素的影响.提出了用实际光学成像系统参数确定频率带宽有限约束的方法.用Knox-Thompson 方法重构初始目标相位形成盲反卷积算法的初始目标函数.研究了一种新颖的有效减小光子噪声、边缘效应和振铃现象的方法.根据最大似然估计理论，用期望最大化的优化方法建立了改进的严格约束多帧盲反卷积算法.模拟图像和实际天文图像的复原结果表明，所建立的多帧盲反卷积，可以有效克服大气湍流和减小光子噪声，改善天文观察图像的分辨率，并部分消除光学系统衍射效应对恢复图像的影响. 关键词： 大气光学 天文观测 图像处理和恢复     

盲目反卷积光谱图超分辨复原算法

反卷积是实现光谱图超分辨复原的重要手段,与常规反卷积相比,盲目反卷积具有不需要预先准确获取卷积核函数的优势。着眼于充分利用光谱信号的特点和已有的光谱图反卷积成果,详细讨论了空域迭代盲目反卷积方法用于光谱图反卷积时的算法实现问题,并在分析光谱图卷积退化过程的基础上,针对光谱图反卷积算法特点,提出了光谱图卷积退化简化计算模型和最小二乘高斯拟合模型,以解决算法中相应的计算问题。基于Matlab平台的仿真表明,对于所用的高斯型谱线和点扩散函数,空域迭代盲目反卷积算法效果良好,在信噪比为50 dB时,分辨率提高约30％。     

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。     

Single frame blind image deconvolution by non-negative sparse matrix factorization

Novel approach to single frame multichannel blind image deconvolution has been formulated recently as non-negative matrix factorization problem with sparseness constraints imposed on the unknown mixing vector that accounts for the case of non-sparse source image. Unlike most of the blind image deconvolution algorithms, the novel approach assumed no a priori knowledge about the blurring kernel and original image. Our contributions in this paper are: (i) we have formulated generalized non-negative matrix factorization approach to blind image deconvolution with sparseness constraints imposed on either unknown mixing vector or unknown source image; (ii) the criteria are established to distinguish whether unknown source image was sparse or not as well as to estimate appropriate sparseness constraint from degraded image itself, thus making the proposed approach completely unsupervised; (iii) an extensive experimental performance evaluation of the non-negative matrix factorization algorithm is presented on the images degraded by the blur caused by the photon sieve, out-of-focus blur with sparse and non-sparse images and blur caused by atmospheric turbulence. The algorithm is compared with the state-of-the-art single frame blind image deconvolution algorithms such as blind Richardson-Lucy algorithm and single frame multichannel independent component analysis based algorithm and non-blind image restoration algorithms such as multiplicative algebraic restoration technique and Van-Cittert algorithms. It has been experimentally demonstrated that proposed algorithm outperforms mentioned non-blind and blind image deconvolution methods.     

Blind deconvolution under band limitation

A blind deconvolution problem is newly stated with the following conditions: the point-spread function is band limited, both the object and the point-spread function are nonnegative, and the solution is to be a diffraction-limited object. A blind deconvolution method was developed that can easily be applied to problems in optics because of the conditions used. The performance of the method is investigated with computer simulations.     

基于噪声特性的大气湍流退化图像多帧盲反卷积复原

由于大气湍流和噪声的影响,造成观测目标图像的退化.为了目标的精确观测,根据噪声特性,结合符合物理意义的约束条件,提出了新的大气湍流图像盲反卷积复原最小化模型,并以共轭梯度数值优化方法交替迭代求解,复原观测目标图像.为验证提出的算法的有效性,在计算机上模拟参数为望远镜口径为2.0 m,大气相干长度为0.1 m,图像信噪比为10 dB的大气湍流退化和噪声污染的图像,以提出的盲反卷积复原方法复原,实验结果表明,提出的盲反卷积复原算法避免了传统的盲反卷积复原算法的缺陷,有效地克服大气湍流和噪声的影响,复原出了清晰的观测目标图像.该图像盲反卷积复原方法的研究,对地基望远镜的观测有重要的基础性作用.     

利用加权预测的图像迭代盲解卷积

为解决大气湍流造成的图像退化问题,本文鉴于现有的盲解卷积算法收敛性不稳定,计算量大等特点,提出了一种基于加权预测的迭代盲解卷积算法。对目前性能优秀的用迭代实现盲解卷积的L-R算法进行优化,在每次迭代结束后通过加权方法求出预测值,根据预测值计算方向加速算子,从而大大提高算法的收敛速度。实验表明:该算法不仅可对模糊退化图像进行很好的复原,同时与L-R算法相比收敛速度提高约43.8倍,其迭代速度快的特点决定了算法具有较高的工程实用价值。     

使用稀疏贝叶斯学习的水声多途信道盲估计

提出了一种使用稀疏贝叶斯学习(SBL)的多途信号盲解卷积方法对水声多途信道的信道脉冲响应(CIR)进行盲估计。该方法利用垂直阵和多频SBL获得宽带舰船声源在不同垂直到达角上的复数域多频点信号,取其相位对垂直阵接收信号匹配滤波,得到每条路径上的CIR,将多路径CIR相干叠加得到最终的多途CIR结果。仿真与海试数据处理结果表明,相比于原有的基于交替投影的多途信号盲解卷积方法,所提出方法有以下几个好处:(1)无需准确预估多途信号数目;(2)分离的多途信号的方位更准确且信号相位更可靠;(3)有效获取了舰船与阵列之间的CIR.并且将弱路径CIR的平均时间估计误差从4.7 ms缩小到1.0 ms.显著提高了弱路径CIR的时间估计精度。使用稀疏贝叶斯学习的多途信号盲解卷积方法能够有效提高多途环境下水声信道盲估计的性能。      

Multi-frame partially saturated images blind deconvolution

When blurred images have saturated or over-exposed pixels, conventional blind deconvolution approaches often fail to estimate accurate point spread function (PSF) and will introduce local ringing artifacts. In this paper, we propose a method to deal with the problem under the modified multi-frame blind deconvolution framework. First, in the kernel estimation step, a light streak detection scheme using multi-frame blurred images is incorporated into the regularization constraint. Second, we deal with image regions affected by the saturated pixels separately by modeling a weighted matrix during each multi-frame deconvolution iteration process. Both synthetic and real-world examples show that more accurate PSFs can be estimated and restored images have richer details and less negative effects compared to state of art methods.     

基于盲解卷积的水声信号恢复技术

信号在环境复杂多途严重的水声波导中传输后,接收到的信号时间长度被拉长,信号是失真的。在许多实际应用中,常常希望从已失真的接收信号中把原始信号波形恢复出来。本文利用盲解卷积技术对水声信号恢复进行研究。理论推演表明,在垂直阵条件下用人造的格林函数可成功地代替水声信道真实的格林函数,可以把非线性关系化解为线性关系,从而推导出依靠基阵记录下的信息去确定声源宽带信号原始波形和环境传播特征的公式和步骤。声场数值计算对6种海底类型进行,恢复后的宽带信号与原始信号的归一化相关系数均大于0.945,对硬海底多途严重的情况,收效特别明显,证明该方法的有效性。在青岛海试中恢复后信号的相关系数平均值为0.933,在青岛海试中利用这种解卷积技术去恢复信号是成功的。     

利用加权预测的图像迭代盲解卷积

为解决大气湍流造成的图像退化问题,本文鉴于现有的盲解卷积算法收敛性不稳定,计算量大等特点,提出了一种基于加权预测的迭代盲解卷积算法。对目前性能优秀的用迭代实现盲解卷积的L-R算法进行优化,在每次迭代结束后通过加权方法求出预测值,根据预测值计算方向加速算子,从而大大提高算法的收敛速度。实验表明：该算法不仅可对模糊退化图像进行很好的复原,同时与L-R算法相比收敛速度提高约43.8倍,其迭代速度快的特点决定了算法具有较高的工程实用价值。     

用神经网络鉴别退化图像的模糊类型

提出了一种用神经网络鉴别退化图像的模糊类型的方法。由于采用不同降质方法得到退化图像的频谱差异较大,以此作为判别依据,用概率神经网络实现了对四种模糊类型:离焦,矩形,运动和高斯模糊的鉴别。根据神经网络的鉴别结果决定点扩散函数的初始估计值,可大大地提高盲解恢复算法的复原质量和系统点扩散函数的估计精度,扩大了算法的实用范围。     

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。     

Relative focus map estimation using blind deconvolution

An automatic focus map extraction method is presented that uses a modification of blind deconvolution for estimation of localized blurring functions. We use these local blurring functions [so-called point-spread functions (PSFs)] for extraction of focus areas on ordinary images. In this inverse task our goal is not image reconstruction but the estimation of localized PSFs and the relative focus map. Thus the method is less sensitive than general deconvolution is to noise and ill-posed deconvolution problems. The focus areas can be estimated without any knowledge of the shooting conditions or of the optical system used.     

Using Single-Channel Blind Deconvolution to Choose the Most Realistic Pharmacokinetic Model in Dynamic Contrast-Enhanced MR Imaging

In dynamic contrast-enhanced magnetic resonance imaging, there has been no consensus in the choice of the pharmacokinetic model. In this paper, a new approach for assessment of the most realistic model for a given tissue is presented. Non-blind and single-channel blind deconvolution algorithms were used in quantitative magnetic resonance dynamic contrast-enhanced imaging of the mouse masseter muscle to compare the realism of two different pharmacokinetic models for the tissue residue function. The first was the adiabatic approximation tissue homogeneity model (aaJW) and the second, the two-compartment exchange model (2CXM). Normals and mice treated with the substance C48/80 were studied. C48/80 increases both blood flow and contrast leakage in muscle substantially. The obtained approximation accuracy was evaluated for both pharmacokinetic models. In addition, the arterial input functions (aifs) estimated using blind deconvolution were compared to the corresponding observed aifs. The hypothesis is that the most realistic model of the tissue residue function leads to the best fits. The non-blind deconvolution did not result in any clear answer. For blind deconvolution, the aifs of the aaJW model were very similar to the corresponding observed aifs, and clearly more so than the aifs of the 2CXM model. Also, the approximation of the observed tracer time sequences was more accurate for the aaJW than the 2CXM model. The realism of different pharmacokinetic models in describing the passage of a tracer through a microvascular bed of a single tissue could be assessed using single-channel blind deconvolution.     

Real data results with wavelength-diverse blind deconvolution

Multiframe blind deconvolution is extended to incorporate simultaneous image acquisition at multiple wavelengths (wavelength diversity). The assumption of common path-length errors across the diversity channels allows for a parallel deconvolution procedure that exploits this coupling. No assumptions about variations in the object's intensity distribution at different wavelengths are required. The method is described and initial results for real images collected with a bench-scale imaging system are presented.     

Application of multi-frame approach in single-frame blind deconvolution

A new single-frame blind deconvolution algorithm for the linear shift-invariant imaging system is presented. The algorithm processes the partial images segmented from one single degraded image by multi-frame approach to recover the point spread function (PSF). Then a deconvolution method is employed to restore the whole image with the recovered PSF. In addition, in order to improve the fidelity and resolution of the recovered PSF, the coprimeness of the partial images is utilized. Results of simulated and real atmospheric turbulence degraded images using the algorithm are reported.