改进的马尔科夫随机场的遥感图像超分辨率复原算法

针对原有使用马尔科夫随机场理论进行超分辨率图像复原,并结合遥感的图像特点,提出了将基于马尔科夫随机场的超分辨率算法应用在遥感的图像复原处理之中。在原有算法的基础上进行改进,为了减少字典存储的训练图像块之间的冗余,采用随机选取训练图像块的方法,同时,通过迭代反投影算法改善图像块之间的拼接痕迹。通过MATLAB仿真,证明改进后的算法与原始算法比较,有更好的复原结果,图像细节能够更好地复原。     

应用双稀疏模型和ADMM优化的图像复原

基于稀疏表示的图像复原算法大都只利用了图像整体稀疏性和局部稀疏性中的一种,未充分利用图像的先验知识,基于此,本文在稀疏表示框架下,同时引入Cosparse解析模型及平移不变小波变换两种稀疏模型,前者对每个图像块进行稀疏表示,后者对整幅图像进行稀疏表示,从而提出一种新的图像复原算法。该算法将图像复原问题归结为双稀疏正则化问题。为求解复杂的双稀疏优化问题,本文运用交替方向乘子法 (ADMM, Alternating Direction Method of Multipliers)算法将该约束优化问题分解为若干子问题,通过交替迭代求解获得复原图像。实验中对不同类型的模糊图像进行了复原,其结果表明该算法对于各类模糊图像的复原比现有复原算法效果更好,从而验证了算法的有效性。      

基于空间约束高斯混合模型的EPLL自然图像复原

为了提高基于块先验的自然图像复原效果,有效去除图像中的噪声和模糊,提出了一种基于空间约束高斯混合模型的块似然对数期望(Expected Patch Log Likelihood, EPLL)复原框架。基于图像块的空间分布信息,将图像块的空间约束高斯混合统计特性作为先验,在图像块复原的基础上实现整幅图像的全局优化复原。对比相关的图像复原方法,提出的方法去噪和去模糊效果更好,并且保图像细节。利用客观性能指标对复原结果进行评价。实验结果表明,提出的方法有效易行,而且复原图像表现出良好的可视效果。     

基于小波变换的高分辨率遥感图像复原算法实现

为了促进遥感图像的后续研究,针对高分辨率遥感图像实现了基于小波变换的迭代收缩（IST）图像复原算法。考虑到算法在复原过程中对内存需求较大,实现过程中采用内存映射文件的方法,将高分辨率遥感图像映射到进程地址空间。针对分块复原图像时通常会伴有边缘跳变现象,影响拼接后的图像质量的问题,使用特殊分块策略对图像进行分块处理。复原算法在VC平台下实现,通过遥感图像复原实验,并对复原图像进行评价分析,复原性能和效率良好。     

调制传递函数在遥感图像复原中的应用

针对空间推扫型全色TDICCD 相机在获取、传输和存储图像的过程中引入模糊和噪声导致图像质量下降的问题,提出了一种基于调制传递函数（MTF）的。感图像复原方法。文中的复原方法是通过图像的MTF 恢复出点扩展函数（PSF）,并将其作为退化函数进行图像复原。通过在MTF 提取前对局部图像用同态滤波算法进行增强处理,加强MTF 提取的抗噪声能力,并使用二次复原的方法避免直接加入增强滤波器导致的MTF 误差。使用均值、对比度及边缘强度等指标对。感图像复原结果进行评价。实验结果表明:复原后图像灰度均值提高45%,平均梯度和边缘强度分别是复原前的3.5和1.75 倍,各项评价指标均优于原始图像,并且MTF 所包含面积MTFA 明显增加,该图像复原算法对提高空间TDICCD 相机图像质量有一定的参考意义。     

基于多层小波变换的压缩感知图像快速复原算法研究

为了能够有效地改善低码率压缩图像的主客观质量,减少图像复原所需观测数据量,节约存储空间和计算量,提出了一种基于多层小波变换的压缩感知图像快速复原算法。该算法将压缩感知理论中的信号重构方法运用于图像复原领域,建立基于压缩感知的图像复原模型,通过少量低维投影空间的测量值并根据信号稀疏表示的先验知识对信号进行精确或高概率的复原。通过Matlab进行实验仿真,结果表明,该算法与传统的图像复原算法相比,通过相同的观测数据量可以获得更高的PSNR,复原效率也得到了提高。     

基于非局部全变差模型的图像复原方法

面向图像复原问题,本文提出一种非局部的全变差图像复原方法。该方法将传统的全变差模型拓展为非局部变差模型,充分利用非局部子块对结构的保持作用,进一步提高复原图像的质量。此外,为了解决上述的非局部全变差模型,本文引入算子运算简化目标函数, 再利用迭代的Splitting算法对其进行交替求解,提高收敛精度。实验结果表明,本文算法在视觉效果和客观评价指标两方面均优于传统算法。     

变参式Tikhonov正则化图像复原算法

正则化方法是近年来流行的图像复原算法。研究了周期边界条件下Tikhonov正则化的预处理共轭梯度算法,提出了新的预处理矩阵和变化正则化参数的方法。正则化参数先取较大值,抑制复原图像中的噪声,得出收敛的结果来修正初始梯度;再取较小值,用来增强复原图像中的细节。对一组图像复原基准问题的实验结果表明,与当前流行的正则化图像复原算法比较,该算法的图像复原效果更佳。     

基于非局部稀疏编码的超分辨率图像复原

基于压缩感知的超分辨率图像复原方法通常采用局部稀疏编码策略,对每一图像块独立编码,易产生人工的分块效应。针对上述问题,该文提出一种基于非局部稀疏编码的超分辨率图像复原方法。该算法在字典训练和图像编码过程中分别运用图像的非局部自相似先验知识,即利用低分辨率图像的插值图像训练字典,并通过计算相似块局部编码的加权平均,得到每一图像块的非局部稀疏编码。仿真实验表明,所提算法能够获得更优的复原效果,并且对于含噪图像具有较强的鲁棒性。     

基于MAP及边缘保持的超分辨率图像复原

MAP方法是解决超分辨率图像复原问题中的一种常用算法,但通常的MAP超分辨率复原方法对于图像的边缘部分没有进行特殊处理,导致最终恢复出来的高分辨率图像的边缘过于平滑.提出了一种局部边缘自适应的先验概率算子,并将其应用于MAP超分辨率图像复原中.与传统方法相比,它具有更好的图像边缘保持能力,因此获得了更佳的视觉效果.     

基于自回归移动平均模型的图像模糊消除并行稳定机制研究

为了克服图像模糊消除算法不稳定与解模糊等难题,保证复原图像的细节信息清晰完整; 并提高算法的运行效率,获取实时性,本文提出了神经网络融合自回归移动平均模型的图像模糊消除并行稳定机制。引入神经网络,基于突触权重系数,构造激活函数;再嵌入人工蜂群算法（ABC- Artificial Bees Colony）,并以神经网络的均方误差函数设计适应度方程,由ABC算法训练神经网络,利用优化后的神经网络来获取自回归移动平均模型的参数;再将自回归移动平均优化模型引入模糊图像,以同时识别模糊函数与模糊图像;并对模糊函数进行相关定义,以消除算法不稳定性与解模糊问题;再对模糊图像进行反卷积,消除模糊。借助仿真实验来测试本文机制的相关性能,结果表明：与其他模糊消除算法相比,该机制的运行速度更快,时耗最短;且本文机制更稳定,模糊消除效果更好,复原图像的细节信息清晰可见。     

Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems

This paper studies gradient-based schemes for image denoising and deblurring problems based on the discretized total variation (TV) minimization model with constraints. We derive a fast algorithm for the constrained TV-based image deburring problem. To achieve this task, we combine an acceleration of the well known dual approach to the denoising problem with a novel monotone version of a fast iterative shrinkage/thresholding algorithm (FISTA) we have recently introduced. The resulting gradient-based algorithm shares a remarkable simplicity together with a proven global rate of convergence which is significantly better than currently known gradient projections-based methods. Our results are applicable to both the anisotropic and isotropic discretized TV functionals. Initial numerical results demonstrate the viability and efficiency of the proposed algorithms on image deblurring problems with box constraints.     

基于分裂Bregman方法的全变差图像去模糊

针对全变差图像去模糊问题,提出一种基于分裂Bregman方法的全变差图像去模糊算法,利用分裂Bregman方法来优化其求解问题模型.首先,利用辅助变量及其二次惩罚泛函把全变差去模糊优化问题转化为一个等价的无约束优化问题;其次,基于Bregman迭代将其分解为两个子优化问题采用交替最小化方法进行求解;最后,根据子问题结构特点,采用离散傅立叶变换及收缩技术实现子优化问题的快速计算.实验结果表明,在不同尺寸模糊核条件下本文算法能获得有效、稳定的图像复原结果,相比FTVd、IRN去模糊方法,本文算法复原效果更好,计算更加快速.     

基于深度多级小波变换的图像盲去模糊算法

近年来卷积神经网络广泛应用于单幅图像去模糊问题,卷积神经网络的感受野大小、网络深度等会影响图像去模糊算法性能。为了增大感受野以提高图像去模糊算法的性能,该文提出一种基于深度多级小波变换的图像盲去模糊算法。将小波变换嵌入编-解码结构中,在增大感受野的同时加强图像特征的稀疏性。为在小波域重构高质量图像,该文利用多尺度扩张稠密块提取图像的多尺度信息,同时引入特征融合块以自适应地融合编-解码之间的特征。此外,由于小波域和空间域对图像信息的表示存在差异,为融合这些不同的特征表示,该文利用空间域重建模块在空间域进一步提高重构图像的质量。实验结果表明该文方法在结构相似度(SSIM)和峰值信噪比(PSNR)上具有更好的性能,而且在真实模糊图像上具有更好的视觉效果。

     

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.     

基于振铃约束的全变差正则化图像去模糊算法

当前去模糊方法只利用图像单一的稀疏特性作为先验信息,忽略了伪边缘(如振铃瑕疵)对模糊核估计的影响,导致其去模糊性能不佳.本文充分利用复杂结构图像的先验信息,设计了振铃约束下的全变差正则化图像去模糊算法.首先,利用多分辨率图像金字塔策略建立多层图像模型,通过对比模糊图像和潜在清晰图像来获得振铃先验信息.其次,将振铃正则约...     

Improved image deblurring based on salient-region segmentation

Image deblurring techniques play important roles in many image processing applications. As the blur varies spatially across the image plane, it calls for robust and effective methods to deal with the spatially-variant blur problem. In this paper, a Saliency-based Deblurring (SD) approach is proposed based on the saliency detection for salient-region segmentation and a corresponding compensate method for image deblurring. We also propose a PDE-based deblurring method which introduces an anisotropic Partial Differential Equation (PDE) model for latent image prediction and employs an adaptive optimization model in the kernel estimation and deconvolution steps. Experimental results demonstrate the effectiveness of the proposed algorithm.     

Blind deblurring of spiral CT images

To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the underlying point spread function (PSF). An oblique CT image can be approximated as the convolution of an isotropic Gaussian PSF and the actual cross section. Practically, the parameter of the PSF is often unavailable. Hence, estimation of the parameter for the underlying PSF is crucially important for blind image deblurring. Based on the iterative deblurring theory, we formulate an edge-to-noise ratio (ENR) to characterize the image quality change due to deblurring. Our blind deblurring algorithm estimates the parameter of the PSF by maximizing the ENR, and deblurs images. In the phantom studies, the blind deblurring algorithm reduces image blurring by about 24%, according to our blurring residual measure. Also, the blind deblurring algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea is substantially improved.     

一种面向目标区域的快速去模糊算法

本文针对监视视频图像中快速移动目标的运动模糊问题,提出一种面向目标区域的快速去运动模糊算法。本文新算法的实现过程可归纳为三步:首先,通过分析图像运动模糊的离散模型,得出去模糊逆运算迭代公式;其次,针对本文实际研究对象,对相邻帧进行差值计算,利用连续两幅帧差图像提取目标运动矢量以及获取模糊目标所在区域;最后,依据图像去运动模糊的离散迭代模型,对模糊目标区域代入运动矢量进行逆运算,实现对目标区域的去运动模糊处理。仿真试验表明,该方法不仅从空间上缩小了去模糊逆运算范围,处理速度快,而且该方法降低了背景中大片随机噪声对去模糊效果的影响,去运动模糊质量好。?      

Motion blur kernel estimation in steerable gradient domain of decomposed image

Blind deblurring, typically underdetermined or ill-posed problem, has attracted numerous research studies over the recent years. Various priors of either the image or the blur kernel are proposed to establish various regularization models to estimate the blur kernel. And sharp edges are often employed as an important clue to recover the blur kernel. However, due to the harmful effects caused by textures and various artifacts, sharp edges are not always beneficial to the kernel estimation. To address this problem, this paper presents a step-edge based blind image deblurring algorithm using steerable gradients. The proposed algorithm adopts a coarse-to-fine multiscale framework with step-edge restoration, kernel estimation and latent image estimation. In each scale, the step-edges are detected and refined through fast image decomposition and thresholding on steerable gradients, while the kernel and latent image are estimated by minimizing the quadratic energy functionals with steerable gradients. Because each of the minimizing functional has a closed-form solution, and can be implemented by using FFTs, our algorithm is also very fast. Experimental results on both synthetic and real data demonstrate that our method outperforms most existing single image blind deblurring methods.