多帧距离选通图像点扩散函数估计的超分辨率重建

为了解决水下激光距离选通图像成像过程中退化模型复杂的难题,提出了利用连续帧图像估计点扩散函数的距离选通超分辨成像方法。首先,从连续帧图像中选取一帧为参考帧作为初始清晰图像,下一帧图像为模糊图像,用梯度约束的方法求出点扩散函数,用于优化清晰图像;然后,依次将后续帧图像当作模糊图像与清晰图像交替迭代求取点扩散函数并优化更新清晰图像;最后获得的清晰图像与参考帧图像用乘法更新的方法估计点扩散函数,结合凸集投影法算法进行超分辨率成像重建。仿真实验结果表明,改进的算法重建图像分辨率和质量明显优于原始的算法。     

基于哈特曼-夏克波前传感器的模糊图像复原方法

离焦模糊图像的清晰度较低,因此必须对其进行复原。传统方法通常采用圆盘或高斯函数来近似离焦造成的点扩散函数,复原效果不够理想。为此,提出利用哈特曼-夏克波前传感器探测离焦波前,根据所得波前计算光学系统的点扩散函数,并采用Richardson-Lucy算法对模糊图像进行复原。搭建了实验用的光学系统,采集了离焦模糊图像以及相应的波前信息,获得了清晰的复原图像,并利用客观图像评价方法对退化图像和复原图像进行了评价,同时与传统方法得到的复原图像进行了比较。实验结果表明,该方法能精确重建点扩散函数,有效提高图像的质量。     

基于随机点扩散函数的多帧湍流退化图像自适应复原方法

针对湍流退化图像随机性的问题,提出了一种基于随机点扩散函数的多帧湍流退化图像自适应复原方法。首先介绍了随机点扩散函数的图像退化模型,并分析了点扩散函数随机性对图像复原造成的影响,建立了基于随机点扩散函数的多帧图像退化模型。在此基础上,建立了基于多帧退化图像的全变分复原模型,利用前向后向算子分裂法对模型进行求解,提高了算法的运算效率。然后,提出了一种新的自适应正则化参数选取方法,该方法利用全变分复原模型的目标函数计算正则化参数,当正则化参数收敛时,复原图像的峰值信噪比达到最大值,因此利用目标函数的相对差值作为自适应算法迭代终止的条件,可以获得最佳复原效果。最后通过实验分析,算法中退化图像的帧数应不大于10帧。实验结果表明:当取10帧退化图像时,AFBS算法运算时间与单帧的FBS算法相当,信噪比增益为1.4 dB。本文算法对图像噪声有明显的抑制作用,对湍流退化图像可以获得较好的复原效果。     

单幅模糊图像点扩散函数估计的梯度倒谱分析方法研究

针对单幅图像复原算法引入先验信息导致复杂度高、运算效率低的问题, 提出了单幅模糊图像点扩散函数估计的梯度倒谱分析方法. 首先给出了单幅模糊图像梯度倒谱估计其点扩散函数的基本原理, 利用相位恢复策略复原了二维点扩散函数相位信息, 实现了点扩散函数的快速估计; 其次, 为鉴别点扩散函数估计精度, 建立了图像梯度保真约束的全变分正则化图像复原模型, 并采用快速稳定收敛的交替方向策略优化能量函数; 通过对仿真和实拍单幅模糊图像进行的测试实验结果表明, 该方法快速准确地估计出点扩散函数, 克服了传统复原算法收敛速度慢的缺点, 有效抑制了振铃效应、保护了边缘信息, 为大尺寸单幅图像复原的工程化实现提供了理论和技术基础. 关键词： 图像复原 点扩散函数 梯度倒谱分析 全变分     

基于图像复原的高光谱图像前向像移补偿

为了提高高光谱图像空间维的图像分辨力,针对航空遥感器成像时由前向像移造成的图像模糊提出了像移补偿方法。分析了航空遥感器前向像移造成图像模糊的退化机制,对运动模糊图像进行了预处理;估计了点扩散函数和噪声功率,使用改进的维纳滤波算法对图像进行复原并以绝对平均误差、峰值信噪比作为评价标准进行了实验。在估计出模糊图像点扩散函数和噪声功率的情况下得到的结果显示:与传统的维纳滤波复原算法相比,改进的维纳滤波复原算法的图像绝对平均误差降低了9.31%,峰值信噪比提高了13.98%,表明提出的算法能够有效改善高光谱图像的像质。     

基于BP神经网络的闪光照相图像复原

针对闪光照相系统模糊较大、成像信噪比较低的问题,提出了一种基于BP神经网络的闪光照相图像复原方法。该方法利用BP神经网络的泛化能力,用样本图像对网络进行训练,建立退化图像与真实图像之间的非线性映射关系,然后将待复原图像分区,利用训练好的BP神经网络对待复原图像的边界区域进行复原处理。数值试验表明,在系统点扩展函数未知的情况下,该算法能较好再现图像边缘信息,复原出的图像在信噪比和视觉方面都有较大提高。     

基于点扩散函数参数辨识的运动模糊图像的盲恢复研究

运动模糊图像恢复的核心是点扩散函数的估计和直接去卷积算法。针对快速运动而形成的低信噪比和小模糊长度图像模糊的问题,提出了一种新的算法来估计模糊核函数的参数,在确定模糊核函数后,模糊图像的恢复采用了一种自然图像梯度统计先验的直接解卷积算法,实验结果证明,与R.Fergus的算法相比较,对于线性运动造成的图像模糊有更快的速度和更好的恢复效果。     

点扩散函数高斯拟合估计与遥感图像恢复

为了减轻或消除航天遥感相机成像过程中图像退化造成的模糊,突出图像的特征目标,对获取的图像进行了恢复处理。首先,采用陷波滤波器在频率域对遥感图像进行了去噪预处理。然后,通过图像中具有刀刃边缘的地物估计成像系统的退化函数,即点扩散函数;同时,利用高斯拟合对估计的点扩散函数进行校正。最后,利用拟合后的点扩散函数,采用自适应维纳滤波对图像进行恢复。实验结果表明：陷波滤波器基本消除了图像中叠加的条带噪声。与原图相比,细节图像恢复后其方差增大4.395,灰度平均梯度增大1.799,Laplacian梯度增大10.014,图像目视效果更清晰。高斯拟合的点扩散函数用于遥感图像恢复,减轻了图像模糊,使图像细节突出,纹理清晰,利于图像的判读和分析。     

联合梯度预测与导引滤波的图像运动模糊复原

针对由相机与所摄景物之间发生相对位置移动所导致的图像运动模糊,提出了一种鲁棒的基于单幅运动模糊图像的盲反卷积算法。该方法首先通过预测图像中的较强边缘信息,实现用简单、易于求解的优化问题在傅里叶域中快速、准确地估计出点扩散函数。然后利用得到的点扩散函数,使用基于梯度约束的非盲反卷积算法复原清晰图像,同时采用一种新的边缘保持滤波器-导引滤波来消除噪声并抑制振铃效应。实验结果表明：本文的算法能够快速地从单幅运动模糊图像复原出具有清晰边缘和纹理的高质量图像,并且运算时间不超过20 s。     

引入泽尼克多项式的极大似然盲解卷积算法初值的优化选取

为提高图像盲复原处理效果,提出了经验法、拟合高斯点扩散函数法,以及符合Kolmogorov谱函数的初值选取方法等三种初值选取方法。引入泽尼克多项式参量化表示点扩散函数,应用极大似然迭代盲解卷积算法对模拟模糊图以及木星观测图进行了复原处理。计算结果表明,符合Kolmogorov谱函数分布的初值方法以及拟合高斯点扩散函数方法得到的图像复原结果较好。     

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。     

Comparison of point spread models for underwater image restoration

It is known that absorption and scattering properties of water are the main causes of blur in underwater images. With the knowledge of point spread function (PSF), the performance of underwater image restoration can be effectively enhanced, which will also extend the imaging range as well. The presented effort reviews several empirical PSF models and an imagery-derived approach based on image formation. Varied models are applied for blind deconvolution restoration, performance of which are compared and discussed. Models under comparison include the empirical models by Duntley, Voss, Wells, as well as the imagery-derived approach which can also provide adequate accuracy and flexibility for image restoration, as shown by experimental results.     

Single image motion deblurring: An accurate PSF estimation and ringing reduction

In this paper we have proposed a single image motion deblurring algorithm that is based on a novel use of dual Fourier spectrum combined with bit plane slicing algorithm and Radon transform (RT) for accurate estimation of PSF parameters such as, blur length and blur angle. Even after very accurate PSF estimation, the deconvolution algorithms tend to introduce ringing artifacts at boundaries and near strong edges. To prevent this post deconvolution effect, a post processing method is also proposed in the framework of traditional Richardson–Lucy (RL) deconvolution algorithm. Experimental results evaluated on the basis of both qualitative and quantitative (PSNR, SSIM) metrics, verified on the dataset of both grayscale and color blurred images show that the proposed method outperforms the existing algorithms for removal of uniform blur. A comparison with state-of-the-art methods proves the usefulness of the proposed algorithm for deblurring real-life images/photographs.     

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.     

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.     

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.     

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.     

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.     

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.     

Blur identification and restoration of degraded images using generalized cross-validation criterion

In this paper, we describe blur identification and restoration of noisy degraded images. The point-spread function (PSF) can be characterized by the quantity of blur. Thus the blur identification problem can be solved as a parameter estimation problem. The estimation method is a generalized cross-validation (GCV) criterion that is known as a powerful measure that can be used to choose the optimal regularization parameter without a priori knowledge about noise. We use the iterative damped-1east squares (DLS) algorithm which is based on the principle of damped least-squares for restoring noisy degraded images.