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.     

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。     

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.     

基于边缘信息的运动模糊图像的鲁棒盲复原

为获得适用于不同模糊图像且简捷的图像盲复原方法,提出了一种稳健的从单幅模糊图像中求取模糊核并对图像去模糊的图像盲复原方法。根据模糊图像与非模糊图之间的边缘关系求模糊核,并在多尺度框架下针对各个子算法设定自适应参数,从而构建一个稳健的图像盲复原方法。对复原结果用4种无参考的图像质量评价方法的评价结果显示,本文方法在噪声和...     

基于变分贝叶斯估计的相机抖动模糊图像的盲复原算法

在曝光过程中由于相机抖动而导致的运动模糊,是一种常见的图像降质现象。该文提出了一种基于变分贝叶斯估计和自然图像梯度统计特性的盲复原算法,用于恢复相机抖动模糊图像,同时针对图像复原过程中出现的振铃效应,设计了一种基于分区域检测和Fuzzy滤波器的去振铃效应方法。实验结果表明,该文提出的盲复原算法能够有效地去除图像中因相机抖动而产生的模糊,而且在保持图像边缘和细节的同时,可以较好地降低振铃效应对图像复原质量的影响。     

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.     

多尺度卷积神经网络的噪声模糊图像盲复原

图像盲复原是从一幅观测的模糊图像恢复出模糊核和清晰图像,传统盲去卷积算法采用简化模型估计模糊核,导致预测模糊核与真实值误差较大,最终复原结果不理想。针对此问题提出一种基于改进残差模块的多尺度卷积神经网络模型,采用端到端模式,无需估计模糊核。提出了一种基于限制网络输入的改进Wasserstein GAN （WGAN）,增加了一层输入限制层,能够限定参数初始值,提高了网络收敛速度。设计了多重损失函数,融合了基于多尺度网络的感知损失和基于条件式生成对抗网络的对抗损失。实验结果表明:所提方法在定量和定性评价指标上优于已有的代表性方法,并且运行速度比相近算法快了4倍。     

TDI遥感图像中畸变像移模糊的去除

采用离轴三反射结构的大视场空间相机存在较大的光学畸变,导致引入时间延迟积分(Time Delay Integration, TDI)技术的面阵探测器在推扫成像时产生像移模糊。根据畸变引起的TDI成像退化原理,将畸变像移模糊转化为非均匀运动模糊,通过求解像移路径计算初始模糊核,将其作为先验信息,建立半盲复原模型进一步细化模糊核。利用初始模糊核复原的粗略图像边缘指导模糊核的细化,提出一种多方向权重异性的全变差模型提取图像结构信息。为了增强先验信息对模糊核细化的约束,构建了含有初始模糊核的正则项,使模糊核的估计不过度依赖于图像内容,采用多尺度迭代方法求解。最后用正则化约束的非盲反卷积方法去除图像模糊。实验结果表明:与现有的几种去模糊算法相比,所提方法的去模糊效果不仅清晰自然且对不同样本图像的模糊核估计更稳定。     

基于迭代交替优化的图像盲超分辨率重建

基于深度卷积神经网络的图像超分辨率重建算法通常假设低分辨率图像的降质是固定且已知的,如双3次下采样等,因此难以处理降质(如模糊核及噪声水平)未知的图像。针对此问题,该文提出联合估计模糊核、噪声水平和高分辨率图像,设计了一种基于迭代交替优化的图像盲超分辨率重建网络。在所提网络中,图像重建器以估计的模糊核和噪声水平作为先验信息,由低分辨率图像重建出高分辨率图像;同时,综合低分辨率图像和估计的高分辨率图像,模糊核及噪声水平估计器分别实现模糊核和噪声水平的估计。进一步地,该文提出对模糊核/噪声水平估计器及图像重建器进行迭代交替的端对端优化,以提高它们的兼容性并使其相互促进。实验结果表明,与IKC, DASR, MANet, DAN等现有算法相比,提出方法在常用公开测试集(Set5, Set14, B100, Urban100)及真实场景图像上都取得了更优的性能,能够更好地对降质未知的图像进行重建;同时,提出方法在参数量或处理效率上也有一定的优势。     

Robust image deblurring with an inaccurate blur kernel

Most existing nonblind image deblurring methods assume that the blur kernel is free of error. However, it is often unavoidable in practice that the input blur kernel is erroneous to some extent. Sometimes, the error could be severe, e.g., for images degraded by nonuniform motion blurring. When an inaccurate blur kernel is used as the input, significant distortions will appear in the image recovered by existing methods. In this paper, we present a novel convex minimization model that explicitly takes account of error in the blur kernel. The resulting minimization problem can be efficiently solved by the so-called accelerated proximal gradient method. In addition, a new boundary extension scheme is incorporated in the proposed model to further improve the results. The experiments on both synthesized and real images showed the efficiency and robustness of our algorithm to both the image noise and the model error in the blur kernel.     

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.     

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.     

基于倒谱与频谱分析的模糊核估计算法

图像盲去模糊是典型的图像和信号处理问题,其目的是从模糊图像中恢复出模糊核及清晰图像。在模糊核估计方面,以往的算法通常将模糊核尺度作为必要的输入参数,近年来有些算法虽然能较准确的估计参数化模糊核,但不能有效估计自然模糊图像中普遍存在的非参数化模糊核。文中利用图像梯度倒谱估计模糊核后再利用频谱分析以进一步精确的估计小尺寸模糊核的尺度。实验结果表明,文中提出的方法能适用于绝大多数场景下自然模糊图像的模糊核尺度估计。     

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 identification of multichannel FIR blurs and perfect imagerestoration

Despite its practical importance in image processing and computer vision, blind blur identification and blind image restoration have so far been addressed under restrictive assumptions such as all-pole stationary image models blurred by zero or minimum-phase point-spread functions. Relying upon diversity (availability of a sufficient number of multiple blurred images), we develop blind FIR blur identification and order determination schemes. Apart from a minimal persistence of the excitation condition (also present with nonblind setups), the inaccessible input image is allowed to be deterministic or random and of unknown color of distribution. With the blurs satisfying a certain co-primeness condition in addition, we establish existence and uniqueness results which guarantee that single input/multiple-output FIR blurred images can be restored blindly, though perfectly in the absence of noise, using linear FIR filters. Results of simulations employing the blind order determination, blind blur identification, and blind image restoration algorithms are presented. When the SNR is high, direct image restoration is found to yield better results than indirect image restoration which employs the estimated blurs. In low SNR, indirect image restoration performs well while the direct restoration results vary with the delay but improve with larger equalizer orders.     

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。     

Iterative image restoration using approximate inversepreconditioning

Removing a linear shift-invariant blur from a signal or image can be accomplished by inverse or Wiener filtering, or by an iterative least-squares deblurring procedure. Because of the ill-posed characteristics of the deconvolution problem, in the presence of noise, filtering methods often yield poor results. On the other hand, iterative methods often suffer from slow convergence at high spatial frequencies. This paper concerns solving deconvolution problems for atmospherically blurred images by the preconditioned conjugate gradient algorithm, where a new approximate inverse preconditioner is used to increase the rate of convergence. Theoretical results are established to show that fast convergence can be expected, and test results are reported for a ground-based astronomical imaging problem     

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.     

A unified approach to superresolution and multichannel blind deconvolution.

This paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Several experiments on synthetic and real data illustrate the robustness and utilization of the proposed technique in real applications.