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。     

贝叶斯迭代联合双边滤波的散焦图像快速盲复原

实现有效的单幅散焦图像盲复原对军事及地质勘测领域的清晰图像获取具有极为重要的意义.常用算法存在计算量大、振铃及噪声敏感的问题,为此本文提出了贝叶斯框架下迭代双边滤波器的快速盲复原算法.它首先用基于深度信息的盲去卷积结果估计点扩散函数的概率模型,进而通过贝叶斯理论构建合理的盲复原最小优化问题;然后推理分析最小优化问题的求解实质,得出双边滤波器快速求解最小优化问题的结论;最后设计迭代联合双边滤波器的求解方式,即利用一次双边滤波器求解的复原结果设计联合双边滤波器的指导图,再将其作为优化问题的输入,迭代实施求解.实验结果表明:该算法能有效抑制振铃,减少计算量,去除噪声,85%图像的像素误差平均值低于0.03,较常用盲去卷积法在同一误差区间的复原成功率提高了19%,运行时间缩短了约78%,能有效用于单幅散焦图像盲复原的实际工程实践中.     

基于维纳滤波的迭代盲解卷积

盲解卷积是在两个卷积因子未知的情况下,通过卷积结果来获知卷积因子的。不考虑噪声,针对高斯模糊图像,在初始估计点扩展函数之后,利用维纳滤波的方法进行频域迭代盲解卷积,达到图像恢复的目的。实验表明,恢复的图像纹理比较清晰,边缘有所改善,主观视觉效果显著。该算法提高了分辨率。     

基于指导滤波的图像盲复原算法

作为图像处理领域的重要分支和研究热点之一, 图像复原方法 的研究始终具有重要理论意义和广泛的应用价值, 图像盲复原一直以来都 是图像复原中比较困难的问题之一. 针对相机与所拍摄景物之间由于相对 位置移动而使所获得图像发生运动模糊的情况, 本文提出了一种基于指导滤波 的图像盲复原算法. 我们首先通过频域迭代算法对点扩散函数 进行估计. 然后, 由于指导滤波具有较好的保持图像边缘的特性, 我们应用基于指导滤波的图像非盲复原算法恢复目标图像. 对以上两步进行反复迭代, 直到获得最终的清晰图像. 为了验证本文所提算法的有效性, 给出了多组对比实验. 实验结果表明, 本文所提算法能够在有效地抑制噪声和振铃 效应的同时, 还能够更好的保持图像的边缘和纹理细节. 因此, 本文算法可以获得更高质量的复原图像.     

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。     

基于模糊度量的激光水下图像复原的盲去卷积方法

由于水体对激光存在着不可克服的吸收和散射效应,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,将基于模糊度量的盲去卷积方法应用于激光水下成像的图像复原中.结合威尔斯小角度近似得出的点扩展函数与调制传递函数,分别讨论了最大期望,最小均方与多次乘法迭代盲去卷积算法,...     

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。     

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。     

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。     

一种用硬件实现的Bayer格式图像恢复算法

针对传统的双线性Bayer彩色图像恢复算法恢复效果不理想,边界部分不明显,局部图像有团块等问题,提出了一种基于硬件实现的Bayer图像快速插值算法。采用梯度算法对Bayer格式图像绿色通道进行恢复,根据像素点所属的颜色组对蓝色通道进行恢复。实验结果表明,本文算法比双线性法有更好的峰值信噪比(PSNR)值,RGB 3个通道的PSNR值均比双线性法高5 dB以上,而且算法消耗时间比双线性法少,恢复的图像视觉效果更好。实验处理一幅512×512的全彩图像仅需要9.3 ms,完全可以满足实时性的要求,因此,本文算法在对实时性要求高的场合有很好的应用前景。     

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.     

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.     

Omni-total variation algorithm for the restoration of all-focused catadioptric image

In this paper, we present a novel omni-total variation (Omni-TV) algorithm for the restoration of defocus blur to obtain all-focused catadioptric image. Catadioptric omni-directional imaging systems usually consist of conventional cameras and curved mirrors for capturing 360° field of view. Mirror curvature in the catadioptric camera often leads to noticeable blurring artifacts in omni-directional imaging. The problem becomes more severe when high resolution sensor is introduced. In an omni-directional image, two points near each other may not be close to one another in the 3D scene. Traditional gradient computation cannot be directly applied to omni-directional image processing. Thus, an omni-gradient computing method combined with the characteristics of catadioptric imaging is proposed, in which an Omni-TV minimization is used as the constraint for deconvolution regularization. The proposed method is vital for improving catadioptric omni-directional imaging quality and promoting applications in related fields like omni-directional video and image processing.     

Motion deblurring using edge map with blurred/noisy image pairs

Motion deblurring methods using blurred/noisy image pairs usually include denoising process of the noisy image. Because both remaining noise and distorted fine details in the denoised image cause an error on deblurring, we propose an algorithm using an edge map of the noisy image to retain sharp edge information while neglecting noise in any smooth region that does not contain information about the motion that occurred during the exposure. In addition, the blur kernel is efficiently estimated by employing the fast total variation regularization method for the gradients of blurred and noisy images only on edge regions. For latent image restoration, another fidelity term is added, which compares the gradients of the noisy and estimated latent images on edge regions to preserve the fine details of the noisy image. To model a sparse distribution of real-world image gradients, a deconvolution method imposing hyper-Laplacian priors based on an alternating minimization scheme is also derived to restore a latent image efficiently. Experimental results show that the peak signal-to-noise ratios of the restored images against the original latent images have been increased by 11.1% on average, when compared to the existing algorithms using an image pair.     

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.     

基于局部位移校正的磁共振图像相干平均

多次扫描相干平均是提高磁共振图像信噪比的常用方法,但如果在多次扫描过程中病人发生自主或不自主的运动,使得图像中的组织发生位移,简单相干平均图像会导致图像模糊.本文受非局域均值算法的启发,提出了一种基于局部位移校正的相干平均方法.该算法通过比较多次采集的图像中组织结构的局部相似性,找出图像间的局部位移,利用该信息修正位移后进行加权平均,从而达到提高图像信噪比的目的.我们用模型及真实的肝脏弥散数据进行了实验.实验结果表明,对于不同次采样间存在运动的磁共振图像,该算法可有效地提高信噪比并保持结构边缘;其结果优于简单的相干平均,去噪效果也优于经典的非局域均值算法.     

A novel modified cepstral based technique for blind estimation of motion blur

In the problem of blind image deconvolution, estimation of blurring kernel is the first and foremost important step. Quality of restored image highly depends upon the accuracy of this estimation. In this paper we propose a modified cepstrum domain approach combined with bit-plane slicing method to estimate uniform motion blur parameters, which improves the accuracy without any manual intervention. A single motion blurred image under spatial invariance condition is considered. It is noted that the fourth bit plane of the modified cepstrum carries an important cue for estimating the blur direction. With the exploration of this bit plane no other post processing is required to estimate blur direction. The experimental evaluation is carried out on both real-blurred photographs and synthetically blurred standard test images such as Berkeley segmentation dataset and USC-SIPI texture image database. The experimental results show that the proposed method is capable of estimating blur parameters more accurately than the existing methods.     

An improved POCS super-resolution infrared image reconstruction algorithm based on visual mechanism

The traditional projection onto convex sets (POCS) super-resolution (SR) reconstruction algorithm can only get reconstructed images with poor contrast, low signal-to-noise ratio and blurring edges. In order to solve the above disadvantages, an improved POCS SR infrared image reconstruction algorithm based on visual mechanism is proposed, which introduces data consistency constraint with variable correction thresholds to highlight the target edges and filter out background noises; further, the algorithm introduces contrast constraint considering the resolving ability of human eyes into the traditional algorithm, enhancing the contrast of the image reconstructed adaptively. The experimental results show that the improved POCS algorithm can acquire high quality infrared images whose contrast, average gradient and peak signal to noise ratio are improved many times compared with traditional algorithm.