一种离焦模糊半径估计的新方法

离焦模糊半径在离焦模糊图像复原中有着十分重要的作用。根据离焦模糊图像的模糊半径与它的傅里叶频谱图的零点暗圆的半径成反比,文中提出了一种基于支持向量机(SVM)的离焦模糊半径估计的新方法。首先,获取已知模糊半径的模糊图像,将模糊图像频谱图第二个暗圆的半径和已知的模糊半径作为训练样本;然后,基于训练样本,通过支持向量机学习得到模糊半径与第二暗圆半径之间的关系模型;最后,利用关系模型来预测未知的模糊半径。实验表明,该方法简单有效,在图像噪声较小时,估计误差率小于0.4%,优于现有的估计方法。     

离焦模糊数字图像的Wiener滤波频域复原

离焦模糊图像的退化模型可用均匀分布的圆盘函数表示 ,其对应的圆盘半径是需辨识的退化模型参数 (即模糊半径 )。利用模糊图像的频率域中的零点位置来估计模糊半径 ,采用简化Wiener滤波对模糊图像进行复原。实验结果表明该方法能够以较少的运算时间代价获取较好的复原效果 ,适用于信噪比高的离焦模糊图像的快速复原     

基于多目标图像清晰度评价的LAMOST光谱仪离焦诊断方法

研究表明,当温度产生一定变化时,会导致大天区面积多目标光纤光谱天文望远镜（LAMOST）光谱仪的内部器件产生热诱导变形,引起CCD靶面上的图像离焦模糊,降低了天文数据测量精度,也大大增加了16台光谱仪的日常维护难度。基于LAMOST运行前拍摄的定标灯谱数据,提出了一种基于多目标图像清晰度评价的离焦诊断方法。该方法通过对不同离焦量下的定标灯谱图像进行分析,提取了一定数量的光斑的半高全宽（FWHM）及其总体分布情况,建立多目标图像清晰度与系统离焦量之间的离焦函数模型,实现对LAMOST成像像质的离焦诊断,为后续智能化主动补偿技术的实现提供了技术支撑。介绍了LAMOST光谱仪的工作原理及结构,给出了光谱仪对调焦精度的需求;详细介绍了光斑FWHM值的测量原理与多目标图像清晰度评价函数的构建方法。与传统图像清晰度评价函数的对比结果显示,文中方法具有更高的清晰度对比率与精度,对定标灯谱图像的离焦诊断误差在10 μm以内,有效降低了人为局部诊断带来的误差,提高了16台光谱仪系统一致性,有望提高LAMOST日常运行效率与光谱仪的长期稳定性。     

图像模糊检测方法

针对数字伪造图像中最常用的模糊操作手段之一的高斯模糊操作,文中提出了一种基于同态滤波和DCT功率谱的图像模糊检测方法.该方法分析了离焦模糊和人工模糊的边界特点,首先利用同态滤波增强人工模糊操作的边缘,并提取增强后图像的二值边缘,从而区分自然模糊和人工模糊,以减小实验中出现的误判,再利用图像的DCT功率谱的分布检测出模糊篡改的区域.该方法能够有效地检测出图像的模糊操作,并进行准确定位.     

基于方向WPS改进TDI CCD遥感图像清晰度评价函数

在轨调焦是航天相机获取高质量图像的关键技术之一。针对航天相机在发射、在轨期间由于振动冲击及温度气压等环境参数变化引起的光学系统离焦现象,以及TDI CCD遥感相机成像场景实时变化的特殊特点,对基于功率谱的清晰度评价函数进行了研究。根据小波变换的多分辨率和带通特性,提出了一种对FFT功率谱的改进小波功率谱（WPS）估计。针对像移亦会导致TDI CCD图像模糊的问题,提出了方向WPS估计算法。参照功率谱地物无关性及离焦会引起功率谱高频分量损失的思路,设计了基于方向WPS的加权清晰度评价函数。实际外场推扫实验结果表明,提出的新清晰度评价函数能有效反映出实际推扫图像的离焦状态,另外相对于FFT功率谱,对场景差异更不敏感,误判率从0.36降低为0,曲线更加饱和。100个仿真样本的平均误判率仅为0.06,满足系统误差要求。因此文中算法满足单调性、灵敏度高、准确度高原则,更适合TDI CCD遥感相机的自动调焦。     

采用Hough变换的离焦模糊参数的估计

在离焦模糊图像的复原过程中,点扩散函数(PSF)常常是未知的,须事先估计其参数。通过分析离焦模糊点扩散函数的频谱特性,提出了一种估计离焦模糊点扩散函数半径的新方法。该方法采用Hough变换原理检测频谱同心圆,进而估计点扩散函数的半径,在一定程度上有效地解决了传统方法估计精度不高的问题。同时,在算法中引入合适的阈值,对阈值变换后的二值频谱图做聚类分析,剔除噪声所带来的奇异点,并充分利用Bessel函数多零点的特点提高抗噪性能。实验结果表明:该方法具有估计精度和稳定性高的优点,能有效地用于图像复原过程中的事先参数估计中。     

一种新的图像清晰度评价函数

离焦模糊图像清晰度评价函数是采用数字图像处理技术实现自动调焦的一个关键．需要不断地提高评价函数的准确性和有效性。深入研究了各种图像梯度的分布情况后发现模糊图像小梯度像素数较大,而清晰图像大梯度的像素数则明显比模糊图像的多,因此可以给梯度加一个阈值．去掉梯度小的值保留梯度大的值,这样可以突出清晰图像的的优势．易于准确判断。首次提出了一种用图像梯度加阈值求和作为由于离焦产生的模糊图像的评价函数,建立了上述评价函数的数学模型．并给出了实验结果和分析。与以往的图像灰度方差、梯度和、小波变换等评价函数相比,给出的评价函数无偏性好、单峰性强,信噪比高,计算量小,在焦平面附近具有变化趋势明显和灵敏度高的特点。     

不同物距下的自动聚焦方法研究

目前对不同物距情况下的自动聚焦问题研究较少，为此提出了解决这一问题的自动聚焦方法。首先分析了不同物距的多个物体之间的成像关系和在不同物距情况下的图像清晰度评价函数的性质，然后提出了一种图像中心窗口的方法和用作图像清晰度评价函数的二维加权DCT系数的方法，最后采用一种改进的搜索策略对图像进行自动聚焦。实验结果表明，该方法可有效地对不同物距的图像进行自动聚焦。     

基于HVS算子的自适应清晰度自动调焦评价函数

传统梯度类自动调焦评价函数对不同清晰度的序列图像不能兼顾灵敏度高和可调焦范围广两种特性,因此提出了基于人类视觉系统(HVS)算子的自适应清晰度自动调焦评价函数。检测图像各方向上的边缘,计算轻微离焦图像函数值;当图像深度离焦时,划分子区域降低采样率后进行清晰度评价。仿真实验表明本文提出的评价函数,轻微离焦位置灵敏度高,对深度离焦区域图像加均值为0,方差为0.001,0.005,0.01的高斯噪声后函数曲线几乎不变,深度离焦区域内可调焦范围大。该评价函数具有灵敏度高、可调焦范围大、抗噪性能良好等特性,更适合粗精结合的搜索算法使用。     

基于神经网络和维纳滤波的半盲离焦图像复原

本文提出了一种基于小波神经网络和维纳(Wiener)滤波的半盲离焦图像复原算法,首先提取训练图像的小波域特点参数向量,将该参数用来训练小波神经网络,利用训练好的网络估计图像离焦模糊参数.由离焦模糊参数获得点扩展函数,然后用Wiener滤波完成图像的复原.实验结果表明:该方法能有效地估计离焦模糊参数和复原模糊图像.     

A recursive soft-decision approach to blind image deconvolution

This paper presents a new approach to blind image deconvolution based on soft-decision blur identification and hierarchical neural networks. Traditional blind algorithms require a hard-decision on whether the blur satisfies a parametric form before their formulations. As the blurring function is usually unknown a priori, this precondition inhibits the incorporation of parametric blur knowledge domain into the restoration schemes. The new technique addresses this difficulty by providing a continual soft-decision blur adaptation with respect to the best-fit parametric structure throughout deconvolution. The approach integrates the knowledge of well-known blur models without compromising its flexibility in restoring images degraded by nonstandard blurs. An optimization scheme is developed where a new cost function is projected and minimized with respect to the image and blur domains. A nested neural network, called the hierarchical cluster model is employed to provide an adaptive, perception-based restoration. Its sparse synaptic connections are instrumental in reducing the computational cost of restoration. Conjugate gradient optimization is adopted to identify the blur due to its computational efficiency. The approach is shown experimentally to be effective in restoring images degraded by different blurs.     

Automatic blur region segmentation approach using image matting

For images with partial blur such as local defocus or local motion, deconvolution with just a single point spread function surely could not restore the images correctly. Thus, restoration relying on blur region segmentation is developed widely. In this paper, we propose an automatic approach for blur region extraction. Firstly, the image is divided into patches. Then, the patches are marked by three blur features: gradient histogram span, local mean square error map, and maximum saturation. The combination of three measures is employed as the initialization of iterative image matting algorithm. At last, we separate the blurred and non-blurred region through the binarization of alpha matting map. Experiments with a set of natural images prove the advantage of our algorithm.     

基于数字图像边缘特性的形态学滤波取证技术

针对数字伪造合成图像中最常用的模糊操作,本文提出了一种基于图像形态学滤波边缘特性的盲取证算法;该方法用同态滤波和形态学滤波的方法增强了模糊操作的图像边缘,分析了离焦模糊和人工模糊边界特点,利用离焦模糊和人工模糊的边缘特性实现了针对数字图像伪造的被动盲取证;实验表明,该方法能够有效地检测伪造数字图像的模糊操作痕迹,并能对已模糊操作的图像局部进行准确定位.     

基于非线性扩散方程的多幅图像复原方法

讨论了光学图像中同时存在噪声与模糊时的多幅图像问题.利用一种能根据边缘方向自适应选取扩散系数的各向异性扩散方程,对图像进行复原.在权函数取值上,根据影响图像复原的降质矩阵和噪声2个因素,构造了合理的权函数.考虑了成像的归一化方程,以减小不同噪声水平的影响;采用仿真方法对不同降质矩阵在相同干扰下的扰动进行估计,获得了降质矩阵的病态程度对复原效果的影响.与传统方法相比,该方法能够选择性地根据噪声和降质获得权值,正确地衡量不同图像对复原问题的贡献,改进处理结果.数值计算结果表明,新方法能获得较传统方法更好的复原图像,权值的选择与单幅图像复原的结果一致.     

Spatially variant defocus blur map estimation and deblurring from a single image

In this paper, we propose a single image deblurring algorithm to remove spatially variant defocus blur based on the estimated blur map. Firstly, we estimate the blur map from a single image by utilizing the edge information and K nearest neighbors (KNN) matting interpolation. Secondly, the local kernels are derived by segmenting the blur map according to the blur amount of local regions and image contours. Thirdly, we adopt a BM3D-based non-blind deconvolution algorithm to restore the latent image. Finally, ringing artifacts and noise are detected and removed, to obtain a high quality in-focus image. Experimental results on real defocus blurred images demonstrate that our proposed algorithm outperforms some state-of-the-art approaches.     

Radon变换和全变分相融合的图像复原算法

图像复原的核心是点扩散函数的估计和直接去卷积算法,针对拍照过程中,相机和被拍摄物体由于相对运动而导致的图像退化问题,提出一种基于Radon变换和全变分相融合的图像复原算法。首先利用radon变换对图像退化模型参数进行估计,然后采用全变分算法复原退化图像,最后在Matlab 2012平台进行仿真实验对算法的性能检验。仿真结果表明,相对于其它图像复原算法,本文算法可以准确估计退化模型参数,获得了更加理想的图像复原效果,具有一定的实际利用价值。     

Quantitative analysis of artifacts in linear space-invariant image restoration

Several image restoration algorithms exist in the literature ranging from deterministic iterative techniques to optimum recursive methods. Unfortunately, all these algorithms produce undesirable artifacts in the process of undoing the degradations because of the ill-posed nature of the image restoration problem. This paper provides a complete quantitative analysis of different artifacts caused by linear shift-invariant (LSI) image restoration methods. The aim of this paper is to mathematically show how these artifacts originate in the general case of an arbitrary blur point spread function and an arbitrary LSI restoration filter, and then to study the characteristics of these artifacts in the special cases of uniform motion blur and out-of-focus blur via experimental analysis. Several pictures that illustrate these artifacts are presented. We discuss strategies for the suppression of these artifacts based on the analysis provided.This paper is based upon research performed under NSF grants MIP-8809291 and CDA-8820693, and Grant No. 88-IJ-CX-0038 from the National Institute of Justice to the University of Rochester.     

Kernel estimation from salient structure for robust motion deblurring

Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the kernel estimation will be removed. Second, we applied a gradient selection method to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation. Third, we proposed a novel kernel estimation method, which is capable of removing noise and preserving the continuity in the kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.