多帧短曝光图像近视解卷积

本文提出一种频域压缩多帧近视解卷积算法.首先,根据波前传感器获取的大气湍流相位估计短曝光图像的初始点扩展函数,并逐步将点扩展函数调整到准确形式.通过共轭梯度(CG)算法交替解卷积频域代价函数估计目标图像和点扩展函数,为了有效压缩信息量,将图像频谱比增加到代价函数中,同时施加基本约束保证迭代快速收敛于全局最小解,采用结构自适应滤波器(SAAF)达到消除噪声的同时保护重建图像细节.实验结果表明算法能够得到高质量的重建图像,性能优于所对比多帧Tikhonov正则化解卷积和多帧Richardson-Lucy的盲解卷积算法.     

基于多帧湍流退化图像的近视解卷积复原

根据贝叶斯和Parseval定理,引入了频域内多帧湍流退化图像的近视解卷积复原算法。以大气湍流长曝光光学传递函数作为估计的光学传递函数。根据频域代价函数的特点,提出分步牛顿法求解代价函数。本算法能够处理未匹配的多帧图像,并能获得理想的复原图像。计算机仿真多帧湍流退化图像的复原结果表明:即使多帧图像未匹配,在不同湍流强度和不同噪声情况下算法仍能复原出好的图像效果。     

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.     

基于振铃抑制的多视频超分辨率重建

为了利用较少低分辨率视频序列实现多视频超分辨率重建,本文提出一种将时空分别进行重建的算法。首先利用已有方法进行时间重建,再以得到的高时间分辨率的视频序列帧为参考帧,结合输入低分辨率视频序列帧进行空间重建。此外,针对传统重建方法在配准不精确的情况下会产生振铃现象这一问题,提出一种加入自适应惩罚项的改进迭代反投影(IBP)算法。实验结果表明,本文算法在输入低分辨率序列较少的情况下,能较好地实现多视频超分辨率重建,且能有效抑制振铃现象;重建出的高分辨率视频序列的结构相似度较对比算法提高3.4%~6.1%;在主观感受上,图像边缘锐利、人工效应少。     

基于循环迭代的多帧湍流退化图像复原算法

采用频域多帧循环迭代解卷积算法(CIBD),针对提高复原图像的准确性和快速性两个方面进行研究。以退化序列中任意帧作为起始帧,逐次增加迭代帧,确保更多的观测帧参与循环迭代解卷积以增加复原的准确性;通过图像间的相关矩阵估计初始点扩展函数(PSF),采用尺度梯度投影法,自适应迭代步长,增加迭代终止条件等措施提高算法的收敛速度。实验结果表明,采用提议的算法能够有效地重建不同大气湍流条件下的远距离观测图像,性能优于传统多帧盲反卷积(MBD)迭代算法。     

复杂背景红外湍流退化图像复原算法研究

针对大气湍流复杂背景红外退化图像的复原问题,构造了各向异性权重差分项,在此基础上将基于二阶差分的2范数极小作为空间相关性约束应用在复杂背景湍流退化图像相邻两帧点扩展函数的优化求解过程中,提出了针对复杂背景的湍流退化图像复原算法。在微机上进行了一系列复原和方法对比实验,实验结果表明本文方法有效,效果较好。     

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。     

基于自适应多字典学习的单幅图像超分辨率算法

自适应字典学习利用图像结构自相似性,将图像自身作为训练样本,通过字典学习使图像中的相似块在字典下具有稀疏表示形式.本文将全局字典学习中利用图像库获取附加信息的思想融入到自适应字典学习的过程中,提出了一种基于自适应多字典学习的单幅图像超分辨率算法,从低分辨率图像自身与图像库同时获取附加信息.该算法对低分辨率图像金字塔结构中的图像块进行聚类,在聚类结果的引导下将图像库中的图像块进行分类,利用各类中的样本分别构建针对各类的多个字典,从而确定表达重建图像块的最优字典.实验表明,与ScSR、SISR、NLIBP、CSSS以及mSSIM等算法相比,本文算法具有更好的超分重建效果.     

结合人眼视觉特性的率失真优化帧刷新算法

通过分析视频图像中位于不同位置上宏块的运动信息以及纹理特征,给出了一种简单而有效的度量宏块视觉敏感度的方法,并在此基础上提出了一种结合人眼视觉特性的率失真优化帧内刷新算法。该算法根据宏块的视觉敏感度对其相应的全局失真进行调整,使之更符合人眼的视觉特性,从而保证具有较大视觉失真期望值的宏块能得到更及时的帧内刷新。典型3G无线信道环境中基于H.264视频的实验结果表明,与传统的率失真优化帧内刷新算法相比,本算法能保证解码器端的重建视频图像具有更好的主观质量。     

重建高分辨率图像的实时串行迭代算法

提出了一种基于多帧低分辨率图像重建高分辨率图像的实时串行迭代算法,在得到与传统多帧算法图像质量相近的同时,大大节省了计算所需的存储空间.给出了该算法的收敛性的证明.计算机模拟结果表明该方法和实验的一致性.最后在一个实际系统验证该方法.     

Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration

We propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for applications with multiframe environments. Since the registration error in each low-resolution image has a different pattern, the regularization parameters are determined adaptively for each channel. We propose two methods for estimating the regularization parameter automatically. The proposed algorithms are robust against registration error noise, and they do not require any prior information about the original image or the registration error process. Information needed to determine the regularization parameter and to reconstruct the image is updated at each iteration step based on the available partially reconstructed image. Experimental results indicate that the proposed algorithms outperform conventional approaches in terms of both objective measurements and visual evaluation.     

空间扩展目标对准误差图像的高分辨复原

大气湍流、光子噪声和光学跟踪系统对准误差严重降低了空间目标观测图像的分辨率.根据最大似然估计原理,建立了提高目标图像分辨率的多帧盲反卷积算法,用共轭梯度优化方法从目标记录图像估计出原始目标函数和点扩散函数.运用低通平滑滤波技术在算法迭代过程中逐步完成对噪声的抑制.模拟实验数据和实际图像的复原结果表明,论文建立的盲反卷积算法有效地克服了大气湍流、光子噪声和光学系统对准误差,提高了目标图像的分辨率,复原目标图像的分辨率达到了光学衍射极限的水平.     

Sparse representation-based algorithm for joint SAR image formation and autofocus

Inaccuracies in the observation model of the synthetic aperture radar (SAR) due to inaccuracies of the velocity and position of the platform or atmospheric turbulence cause degradations in reconstructed images which necessitate the use of autofocus algorithms. In this paper we propose a novel signal processing algorithm for joint SAR image formation and autofocus in a synthesis dictionary based sparse representation framework. Proposed algorithm can be applied broadly to scenes that exhibit sparsity with respect to any dictionary. This is done by extending our previously developed sparse representation-based SAR imaging framework to joint SAR image formation and autofocus. To this end, the phase error vector is separated from the unknown phase of the complex-valued back-scattered field. Phase error vector is estimated using a MAP estimator and compensated through an iterative algorithm to produce focused images. We demonstrate the effectiveness of the proposed approach on synthetic and real imagery.     

数字图像空间分辨率改善的插值--模拟采样迭代方法

在遥感图像和计算机视觉的一些应用中,需要从已有的一些低分辨率图像来获得更高分辨率的图像.已有一些方法都是针对大小完全相同和图像之间不存在局部的几何畸变(或假设如此)的图像,而对从大小不同或分辨率不同的图像和图像之间存在相对的几何畸变等更普遍存在的实际图像来重构更高分辨率图像的问题未加涉及.本文用从带限信号的非均匀采样进行信号重构的迭代方法和凸集投影方法出发,导出了一种应用范围更加广泛的从多幅空间分辨率低的图像重构更高空间分辨率图像的一般性空间域插值-模拟采样迭代方法,并提出了凸集投影的变权迭代解法.该迭代方法可以对付图像之间存在相对的几何畸变、辐射亮度差异、空间分辨率差异及图像存在噪声等情形.实验表明,该迭代解法具有很好的收敛性和很好的收敛效果.     

Convergence properties of Gauss-Newton iterative algorithms in nonlinear image restoration

Nonlinear image restoration is a complicated problem that is receiving increasing attention. Since every image formation system involves a built-in nonlinearity, nonlinear image restoration finds applications in a wide variety of research areas. Iterative algorithms have been well established in the corresponding linear restoration problem. In this paper, a generalized analysis regarding the convergence properties of nonlinear iterative algorithms is introduced. Moreover, the applications of the iterative Gauss-Newton (GN) algorithm in nonlinear image restoration are considered. The convergence properties of a general class of nonlinear iterative algorithms are rigorously studied through the Global Convergence Theorem (GCT). The derivation of the convergence properties is based on the eigen-analysis, rather than on the norm analysis. This approach offers a global picture of the evolution and the convergence properties of an iterative algorithm. Moreover, the generalized convergence-analysis introduced may be interpreted as a link towards the integration of minimization and projection algorithms. The iterative GN algorithm for the solution of the least-squares optimization problem is introduced. The computational complexity of this algorithm is enormous, making its implementation very difficult in practical applications. Structural modifications are introduced, which drastically reduce the computational complexity while preserving the convergence rate of the GN algorithm. With the structural modifications, the GN algorithm becomes particularly useful in nonlinear optimization problems. The convergence properties of the algorithms introduced are readily derived, on the basis of the generalized analysis through the GCT. The application of these algorithms on practical problems, is demonstrated through several examples.     

Studies of the correction for phase diverse speckle

Phase diverse speckle is a novel kind of imaging technique and can be used to overcome image degradation from unknown phase aberrations, such as atmospheric turbulence. The wave-front phase expanded on the Zernike polynomials is esti- mated from a pair of images (in the focal and out of focus planes). In this paper the principle of PDS is analyzed, and genetic algorithm is used as the iterative algorithm to simulate some characteristics, such as the influence of Zernike polynomials’ mode, amplitude of turbulence on the phase estimation. Thus, a new method for recovery of images is explored.     

迫近算子在MR图像快速重建中的应用研究

 将迫近算子用于求解基于压缩感知理论的磁共振图像快速重建模型,得到了一个高效的迭代重建算法.将该算法用于部分K空间数据重建,并就算法对噪声的敏感性及算法对迭代初值的依赖性进行了仿真实验.实验结果表明,算法对噪声不敏感,对初值也没有显著的依赖性,该算法可由极少量K空间数据重建出高质量的MR图像.     

Multiframe super-resolution based on half-quadratic prior with artifacts suppress

The multiframe super-resolution (SR) technique aims to obtain a high-resolution (HR) image by using a set of observed low-resolution (LR) images. In the reconstruction process, artifacts may be possibly produced due to the noise, especially in presence of stronger noise. In order to suppress artifacts while preserving discontinuities of images, in this paper a multiframe SR method is proposed by involving the reconstruction properties of the half-quadratic prior model together with the quadratic prior model using a convex combination. Moreover, by analyzing local features of the underlined HR image, these two prior models are combined by using an automatically calculated weight function, making both smooth and discontinuous pixels handled properly. A variational Bayesian inference (VBF) based algorithm is designed to efficiently and effectively seek the solution of the proposed method. With the VBF framework, motion parameters and hyper-parameters are all determined automatically, leading to an unsupervised SR method. The efficiency of the hybrid prior model is demonstrated theoretically and practically, which shows that our SR method can obtain better results from LR images even with stronger noise. Extensive experiments on several visual data have demonstrated the efficacy and superior performance of the proposed algorithm, which can not only preserve image details but also suppress artifacts.     

基于迭代算法的可验证视觉密码

通过改变验证图像的分享和恢复方式,该文提出了一种基于迭代算法的可验证视觉密码方案。该方案设计专用算法分享验证图像,利用算法的迭代优化验证过程,不仅大幅减小了像素扩展度,而且显著提高了验证效率。同时,通过引入异或操作实现了验证图像的完全恢复。