Single image deblurring using novel image prior constraints

Single image deblurring is a highly ill-posed problem and requires to be regularized. Many common forms of image prior have a major drawback that is unable to make full use of local image information. In this paper, we propose a single image deblurring method using novel image prior constraints. We establish a probabilistic model by enforcing inspired image prior constraints and adopt an advanced iterative scheme that alternates between blur kernel estimation and non-blind image restoration. To suppress ringing artifacts caused by inevitable blur kernel estimated errors, our method employs total variation image restoration and presents an alternation half-quadratic algorithm to solve the non-convex cost function. Finally, experiments show that our method has good performance in suppressing ringing artifacts, and makes a good balance between alleviating staircase effects and preserving image details.     

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.     

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。     

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。     

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.     

基于去模糊空间变换RCNN的毫米波图像目标检测（英文）

提出一种包含去模糊的空间变换区域卷积神经网络的目标检测算法.首先,基于主动毫米波圆柱扫描成像原理对人体进行三维成像(频率24~30 GHz),建立毫米波图像数据集.然后,估计毫米波图像的模糊核,通过卷积去噪网络获得图像先验知识,将其集成到半二次分裂的优化方法中,以实现非盲目去模糊.最后,由定位网络、网格生成器和采样网络三部分组成空间变换网络,将它融入到特征提取网络中,在去模糊后实现目标检测.通过该非盲目去模糊算法得到的图像的峰值信噪比可达27.49 dB,目标检测算法的平均精度可达80.9%.实验结果表明,与现有的先进方法相比,该方法可以有效地提高图像质量和检测精度,为毫米波图像中隐藏危险品的目标检测提供了新的技术支持.     

An improved Richardson-Lucy algorithm based on local prior

Ringing is one of the most common disturbing artifacts in image deconvolution. With a totally known kernel, the standard Richardson-Lucy (RL) algorithm succeeds in many motion deblurring processes, but the resulting images still contain visible ringing. When the estimated kernel is different from the real one, the result of the standard RL iterative algorithm will be worse. To suppress the ringing artifacts caused by failures in the blur kernel estimation, this paper improves the RL algorithm based on the local prior. Firstly, the standard deviation of pixels in the local window is computed to find the smooth region and the image gradient in the region is constrained to make its distribution consistent with the deblurring image gradient. Secondly, in order to suppress the ringing near the edge of a rigid body in the image, a new mask was obtained by computing the sharp edge of the image produced using the first step. If the kernel is large-scale, where the foreground is rigid and the background is smoothing, this step could produce a significant inhibitory effect on ringing artifacts. Thirdly, the boundary constraint is strengthened if the boundary is relatively smooth. As a result of the steps above, high-quality deblurred images can be obtained even when the estimated kernels are not perfectly accurate. On the basis of blurred images and the related kernel information taken by the additional hardware, our approach proved to be effective.     

High quality non-blind image deconvolution using the Fields of Experts prior

In this paper, we propose a non-blind image deconvolution method under Bayesian probabilistic framework. A robust prior, i.e., the Fields of Experts (FoE) prior which is learned from natural images is adopted to regularize the latent image. To solve the resulted optimization problem, the Split Bregman algorithm is utilized. We use a data set to verify the effectiveness of the proposed approach. Experimental results show that the restored images are of higher quality than that of some state of the art algorithms.     

Non-blind image deconvolution using natural image gradient prior

Since non-blind image deconvolution is inherently ill-posed, the results of unregularized methods are often contaminated by noise and ringing artifacts. To reach a stable solution, we adopt the natural image gradient prior to regularize the latent image and obtain an improved version of the Richardson–Lucy (RL) algorithm. We use both synthetic and real world blurred images to test the proposed method. Experimental results show that the negative artifacts are significantly suppressed and the restored images are of high quality.     

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。     

核密度估计时-空域滤波红外背景抑制方法

提出一种基于核密度估计的时-空域滤波算法,用于红外搜索跟踪系统图像的背景抑制。算法分为空域滤波和时域滤波两部分。在空域滤波中,采用核密度估计算法对背景进行平滑;在时域滤波中,采用核密度估计算法对经过空域滤波后的图像灰度值进行概率计算,判别属于背景残差的灰度值,然后做进一步的滤除。核方法对背景有很好的光滑性且易于计算机实现,实验表明,这种非参方法设计的时-空域滤波算法对背景杂波有非常良好的抑制效果,信噪比也得到明显提高。     

最近邻域与无邻域算法结合空间滤波实现显微光切片

运用最近邻域与无邻域算法,并结合空间滤波方法,可去除显微切层图像的离焦模糊成份,可获得高成像速度、高质量的显微光切片。对各种显微图像进行了光切片处理,并与由现有去卷积处理、结构光照明等方法获得的光切片图像进行了对比,验证了该算法在去除模糊信息方面有更好的效果,提高了图像处理效率,获得了令人满意的光切片图像。在此基础上研制出了显微光切片图像处理系统。运用最近邻与无最近邻算法,结合空间滤波方法,采用简便可调的卷积模糊算子代替点扩散函数的估计过程,图像处理方便快捷,可较好地去除离焦平面的模糊信息。     

Semi-blind image restoration based on Chan-Vese denoising model

A semi-blind image restoration algorithm is proposed based on reduced non-convex approximation of Luminita Vese and Tony Chan's (C-V) denoising model. Compared with C-V denoising model, we modify the fidelity term and add a term on point spread function (PSF). The function depends on two variables: the image function to be restored u and the standard deviation of Gaussian kernel to be estimated σ. Then the problems consist in solving a system with two coupled equations. Compared with the Leah Bar's semi-blind image restoration model which must solve three coupled equations, our method only needs to solve two equations. Furthermore, the estimation of f by our algorithm is superior to Leah Bar's algorithm. The experimental results demonstrate that the proposed method is effective.     

盲反卷积方法在水下激光图像复原中的应用

由于水体对激光存在着吸收和散射效应,距离选通水下激光成像系统所获得的图像存在不同程度的劣化问题,具有信噪比低、边缘模糊等特点。为提高图像质量,在分析水下激光成像劣化过程的基础上,研究了水下激光图像的基本噪声特征,并结合点扩展函数和调制传递函数,利用威尔斯小角度近似理论,将盲反卷积方法应用到水下激光图像复原中。在进行盲反卷积图像复原时,比较和讨论了将原始图像和经过降噪处理后的图像分别作为初始输入的处理结果;并对当人为改变调制传递函数和点扩展函数时所得到的图像复原结果进行了研究和讨论。处理结果表明该方法能达到抑制背景噪声、突出目标细节、提高对比度的效果,对水下激光图像增强十分有效。     

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.     

基于多相组重建的航空图像超分辨率算法

为提高航空图像的空间分辨率, 提出一种基于多相组重建的超分辨率算法. 融合图像间的互补信息, 将多帧低分辨率图像作为图像基, 参考帧分解为多相组, 利用差异采样特性构建图像基与参考帧之间的的多相组线性关系重建得到高分辨率图像的多项组, 经图像多相分解逆变换获得融合的高分辨率图像. 根据该融合图像的局部内容和结构信息自适应调整控制核核函数, 应用改进的控制核回归算法去除图像模糊和噪声得到清晰的超分辨率图像. 与传统算法相比, 该算法无需图像配准和迭代过程, 计算效率极大地提高. 实验结果表明, 本文算法能够有效提高航空图像的空间分辨率, 在定量评价指标和主观视觉效果方面都有显著提高.     

近场反卷积聚焦波束形成声图测量

为了提高声图测量中对多个声源的分辨能力和定位精度,给出了一种近场二维反卷积聚焦波束形成声图测量方法。推导了水下声图测量的广义卷积模型,根据声图测量中点传播函数移变但可预测的特点,通过预存点传播函数字典的方式,将波束形成过程中的卷积问题转化成叠加积分问题,并应用二维Richardson-Lucy迭代算法实现了二维移变模型情况下的近场二维反卷积求解,从而实现高分辨声图测量。通过仿真和海试对比了反卷积、常规声图测量和MVDR声图测量的性能,结果表明反卷积算法在500次迭代情况下聚焦峰尺度小于另外两种算法的1/2,旁瓣级下降超过6 dB.      

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.     

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.     

Dynamic deformation image de-blurring and image processing for digital imaging correlation measurement

This paper proposes a method for de-blurring of images captured in the dynamic deformation of materials. De-blurring is achieved based on the dynamic-based approach, which is used to estimate the Point Spread Function (PSF) during the camera exposure window. The deconvolution process involving iterative matrix calculations of pixels, is then performed on the GPU to decrease the time cost. Compared to the Gauss method and the Lucy–Richardson method, it has the best result of the image restoration. The proposed method has been evaluated by using the Hopkinson bar loading system. In comparison to the blurry image, the proposed method has successfully restored the image. It is also demonstrated from image processing applications that the de-blurring method can improve the accuracy and the stability of the digital imaging correlation measurement.