基于图像先验和结构特征的盲图像复原算法

提出了一种基于图像先验和图像结构特征的盲图像复原算法,在模糊核未知的情况下,采用一系列离散化的模糊核参数对模糊图像进行非盲去卷积,得到一系列对应的复原图像。同时提出一种复原图像判决准则,对这一系列复原图像进行质量判决,从中得到最优的复原图像。最后在实验部分,通过对图像的测试表明,提出的盲图像复原算法能较准确的得到最优复原图像,复原效果在主观和客观标准上均有良好表现。     

空间变化离焦模糊红外图像快速复原算法

为了提升空间变化离焦模糊红外图像的图像质量,提出了一种基于图像质量评价的快速复原算法。本文提出的方法首先对模糊图像采用不同点扩散函数对应的截断约束最小二乘法算法进行复原而获得多幅复原图像,并对复原图像进行去振铃;然后对复原图像中每个像素为中心的区域进行图像质量评价,将采用不同参数复原的图像以图像质量评价的结果进行组合以获得最终的复原图像。由于无需对模糊图像点扩散函数估计,且采用了空间域运算的截断约束最小二乘法算法进行图像复原,实验结果表明,本文提出的算法能够对空间变化离焦模糊红外图像进行快速复原,算法运行速度较基于点扩散函数估计的方法大幅提升。     

湍流退化红外图像校正算法

针对高速湍流场引起的红外图像模糊问题,提出了一种基于改进增量Wiener滤波的复原校正算法.首先,基于先验知识对湍流退化图像的降晰函数进行辨识并得到复原图像的起始估计;其次,提取起始复原图像中的强边缘并平滑边缘区域;最后,利用改进的增量维纳滤波算法迭代复原图像.实验结果表明:该算法与传统的迭代盲复原算法及基于Fuzzy滤波器的后期去振铃算法相比,复原图像的振铃测度有较大下降,同时提高了复原图像的质量,降低了算法的时间复杂度.     

基于图像稀疏表达的模拟退火图像复原

针对现有的图像复原方法振铃效应严重的问题,提 出了一种基于图像稀疏表达的模拟退火的图像复原方法来恢复模糊图像。首先根据模拟退火 算法的要求,建立价格函数并 通过图像的模糊因子确定参数;然后在价格函数中的约束项引入图像的稀疏性,以提高复原 图像的质量;接着给初始解一个随机扰动,产生扰动解,并根据扰动解所造成的价格函数 的变化判断是否接受该扰动解;最后,当价格函数小于某一预设值时所得到的解即为复原 图像。实验结果表明,复原后图像细节增加且振铃效应明显减少,相对于目前已有的复原方 法,峰值信噪比(PSNR)平均提高了2~3dB。恢复效果表明,本文方法具有较大的实用价值。     

匀速直线运动模糊图像复原的改进算法

提出了一种改进的运动模糊图像复原算法,先用方向微分思想鉴别出运动模糊方向,然后采用求微分模糊图像自相关函数的方法鉴别模糊尺度,从而构造出最为近似的点扩散函数（Point Spread Function,PSF）。针对振铃效应问题,用最优窗法对图像进行处理,最后利用维纳滤波法与图像均衡法相结合的改进算法对运动模糊图像进行复原。实验结果表明,该算法可以取得较好的复原效果。     

基于支持向量机的湍流退化图像加速复原算法

由于湍流图像的退化原因十分复杂,现有图像复原算法很难在复原效率和复原质量间达到很好的平衡,为此提出了一种基于支持向量机的湍流退化图像加速复原算法.该算法通过设置方差阈值进行样本选择,舍弃了冗余信息、提高了样本质量;同时,对序列图像进行实时模型更新,加快了序列图像的复原速度.针对电弧风洞图像,将加速复原算法和原算法进行了比较.实验结果表明,加速算法的复原速度更快、复原效果也更好,它可以有效地解决湍流退化给图像带来的噪声和能量衰减问题,并能很好地校正湍流效应引起的模糊和抖动现象.     

基于各向异性总变分的运动模糊盲复原方法

针对目前运动模糊图像盲复原算法对图像边缘中 拐角结构复原不佳这一问题,提出了一种以各向异性总变分为图像和模糊核正则项的遥感图 像盲 复原方法。为便于进行数值计算,采用交叉算法和分裂布雷格曼迭代导出了本文提出的盲复 原方法的迭 代公式。实验结果表明,与基于同向异性总变分的盲复原方法以及基于小波框架的盲复原方 法相 比,本文方法不仅能估算出较精确的点扩散函数(PSF,point sp read function),有效地 去除图像的模糊效应,而且对图像边缘结构特别是拐角边缘结构的增强有着独特的优势。     

基于先验信息和正则化技术的图像复原算法的研究

在湍流退化图像复原研究中,为了消除大气湍流的影响,提出了一种基于先验信息和正则化技术的盲解卷积图像复原算法.该算法是以极大似然估计为基本原理,将目标图像和点扩展函数的先验信息以惩罚项的形式引入到极大似然函数中,同时利用正则化技术优化目标图像和点扩展函数的估计过程,以增加极大似然估计算法的收敛性和稳定性.通过退化图像的复原实验结果表明,该算法在退化模型完全未知的情况下,可以有效的实现对湍流退化图像的盲复原.     

基于有理数多项式先验模型的图像盲去模糊

图像盲去模糊问题是当今图像处理领域的热点问题之一.基于混合高斯先验模型的变分贝叶斯去模糊算法可以有效地复原模糊图像,成为一种重要的图像去模糊算法.虽然混合高斯先验模型可以很好地逼近自然图像的梯度分布,但是该模型在图像梯度值较大处往往会产生过拟合导致去模糊后的图像产生振铃效应,严重影响了图像可读性.利用有理数多项式先验模型代替混合高斯模型逼近自然图像的梯度分布,克服算法的上述缺点.有理数多项式函数的分母多项式强制函数在梯度值较大值时平滑,所以有效地避免了过拟合现象的发生,从而使得模糊核估计得更准确,减少振铃效应.实验结果表明了算法的可行性和有效性.     

基于头脑风暴优化算法的BP神经网络模糊图像复原

该文提出一种基于头脑风暴智能优化算法的BP神经网络模糊图像复原方法(OBSO-BP)。该方法在聚类和变异两方面优化了头脑风暴智能算法,利用头脑风暴优化算法易于解决多峰高维函数问题的特点,自动搜寻BP神经网络更佳的初始权值和阈值,以减少BP网络对其初始权值和阈值的敏感性,避免网络陷入局部最优解,增加网络的收敛速度,减小网络误差,提高图像还原质量。该文采用20张不同的图像,对其模糊图像分别进行维纳滤波复原(Wiener)、基于头脑风暴算法的维纳滤波复原(Wiener-BSO)、BP神经网络复原以及基于头脑风暴算法的BP神经网络(BSO-BP)图像复原实验。实验结果表明,该方法能够取得更好的图像复原效果。     

Blind image deconvolution for single motion-blurred image

Motion blur due to camera shake during exposure is one of the most common reasons of image degradation,which usually reduces the quality of photographs seriously.Based on the statistical properties of the natural image's gradient and the blur kernel,a blind deconvolution algorithm is proposed to restore the motion-blurred image caused by camera shake,adopting the variational Bayesian estimation theory.In addition,the ring effect is one problem that is not avoided in the process of image deconvolution,and usually makes the visual effect of the restored image badly.So a dering method is put forward based on the sub-region detection and fuzzy filter.Tested on the real blurred photographs,the experimental results show that the proposed algorithm of blind image deconvolution can remove the camera-shake motion blur from the degraded image effectively,and can eliminate the ring effect better,while preserve the edges and details of the image well.     

Robust penalty-weighted deblurring via kernel adaption using single image

Image blind deconvolution is well known as a challenging, ill-posed problem due to the uncertainty of the blur kernel and the noise condition. Based on our observations, blind deconvolution algorithms tend to generate disconnected and noisy blur kernels, which would yield a serious ringing effect in the restored image if the input image is noisy. Therefore, there is still room for further improvement, especially for noisy images captured under poor illumination conditions. In this paper, we propose a robust blind deconvolution algorithm by adopting a penalty-weighted anisotropic diffusion prior. On one hand, the anisotropic diffusion prior effectively eliminates the discontinuity in the blur kernel caused by the noisy input image during the process of kernel estimation. On the other hand, the weighted penalizer reduces the speckle noise of the blur kernel, thus improving the quality of the restored image. The effectiveness of the proposed algorithm is verified by both synthetic and real images with defocused or motion blur.     

An adaptive two phase blind image deconvolution algorithm for an iterative regularization model

This paper proposes a blind image deconvolution method which consists of two sequential phases, i.e., blur kernel estimation and image restoration. In the first phase, we adopt the L₀-norm of image gradients and total variation (TV) to regularize the latent image and blur kernel, respectively. Then we design an alternating optimization algorithm which jointly incorporates the estimation of intermediately restored image, blur kernel and regularization parameters into account. In the second phase, we propose to take the mixture of L₀-norm of image gradients and TV to regularize the latent image, and design an efficient non-blind deconvolution algorithm to achieve the restored image. Experimental results on both a benchmark image dataset and real-world blurred images show that the proposed method can effectively restore image details while suppress noise and ringing artifacts, the result is of high quality which is competitive with some state of the art methods.     

An improved image blind deblurring based on dark channel prior

In order to solve the ringing effect caused by the incorrect estimation of the blur kernel, an improved blind image deblurring algorithm based on the dark channel prior is proposed. First, in the blur kernel estimation stage, high-pass filtering is introduced to enhance the image quality and enhance the edge information to make the blur kernel estimation more accurate. A combination of super Laplacian prior and dark channel prior is introduced to estimate the potential clear image. Then the accurate blur kernel is estimated through alternate iterations from coarse to fine. In the image restoration stage, a weighted least square filter is introduced to suppress the ringing effect of the original clear image to further improve the quality of image restoration. Finally, image deconvolution based on Laplace priors and L₀ regularized priors is used to restore clear images. Experimental results show that our approach improves the peak signal-to-noise ratio(PSNR) by about 0.4 d B and structural similarity(SSIM) by about 0.01, respectively. Compared with the existing image deblurring algorithms, this method can estimate the blur information more accurately, so that the restored image can achieve the effect of keeping the edges and removing ringing.     

Total variation blind deconvolution employing split Bregman iteration

Blind image deconvolution is one of the most challenging problems in image processing. The total variation (TV) regularization approach can effectively recover edges of image. In this paper, we propose a new TV blind deconvolution algorithm by employing split Bregman iteration (called as TV-BDSB). Considering the operator splitting and penalty techniques, we present also a new splitting objective function. Then, we propose an extended split Bregman iteration to address the minimizing problems, the latent image and the blur kernel are estimated alternately. The TV-BDSB algorithm can greatly reduce the computational cost and improve remarkably the image quality. Experiments are conducted on both synthetic and real-life degradations. Comparisons are also made with some existing blind deconvolution methods. Experimental results indicate the advantages of the proposed algorithm.     

基于低秩稀疏分解的湍流退化图像序列的盲去卷积算法

针对湍流退化图像序列存在像偏移、像抖动和像 模糊的问题,提出一种基于低秩稀疏分解和多帧去 卷积的图像复原算法。首先分析大气湍流下图像序列的退化特征,然后在低秩稀疏分解的思 想下,采用非增广拉格朗日乘子(IALM)法优化由低秩 矩阵的核范数和稀疏 矩阵的Frobenius范数之和构成的目标函数,将湍流退化序列分解为低秩稳像和稀疏湍流两 部分;最后利用 多帧去卷积算法复原对齐的稳像。实验结果表明,本文算法能够有效校 正湍流像素偏移,在提高复原质量和速度方面取得了明显的效果。     

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.     

Robust multichannel blind deconvolution via fast alternating minimization

Blind deconvolution, which comprises simultaneous blur and image estimations, is a strongly ill-posed problem. It is by now well known that if multiple images of the same scene are acquired, this multichannel (MC) blind deconvolution problem is better posed and allows blur estimation directly from the degraded images. We improve the MC idea by adding robustness to noise and stability in the case of large blurs or if the blur size is vastly overestimated. We formulate blind deconvolution as an l(1) -regularized optimization problem and seek a solution by alternately optimizing with respect to the image and with respect to blurs. Each optimization step is converted to a constrained problem by variable splitting and then is addressed with an augmented Lagrangian method, which permits simple and fast implementation in the Fourier domain. The rapid convergence of the proposed method is illustrated on synthetically blurred data. Applicability is also demonstrated on the deconvolution of real photos taken by a digital camera.     

Blind deconvolution using a variational approach to parameter, image, and blur estimation.

Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods.     

Blind deconvolution of spotted image based on slice selection of third-order moment

The 1D blind deconvolution algorithm using maximum time delay slice of the third-order moment ((MTDS-TOM) [Lu, W]) is extended to 2D blind deconvolution for spotted image deblurring. A scaled and shifted version of the image is obtained using a special slice selected from its third-order moment, which is estimated using a 4D blind deconvolution. An application of the proposed method for removing the optical blur of a microarray image is given.