利用总变分最小化方法的无监督纹理图像分割

纹理图像的分割是图像处理领域中的一个典型难题。不同于传统的提取纹理特征量进行纹理分割的方法，本文将图像复原和重建中的总变分最小化方法和活动围道分割方法相结合，提出了一种简单的线性纹理模型。利用总变分最小化方法在保持图像大尺度棱边信息的基础上对纹理体现的局部小尺度周期性灰度振动细节进行平滑得到简化的图像原型。对其进行分割获得不同纹理区域之间的低定位精度的边界围道，再利用原始图像对围道进行高精度细化。在总变分最小化导致的非线性扩散方程求解过程中，运用AOS(additive operatorr splitting)数值算法以改进算法效率。实验结果表明，该方法能很快提取出纹理图像的简化图像，同时是一种无监督的纹理分割方法。     

全变差最小化在CT图像环状伪影去除中的应用

环状伪影是多种CT图像中普遍存在的一种伪影,主要由像元响应不理想等因素引起。全变差最小化是一种广泛使用的去噪方法,将全变差最小化应用于CT图像,在模拟投影数据中人为添加噪声并重建,使用全变差最小化进行处理,观察图像环状伪影的去除情况,并得出了图像峰值信噪比随全变差最小化次数的变化曲线。     

Single image rain and snow removal via guided L0 smoothing filter

Since no temporal information can be exploited, rain and snow removal from single image is a challenging problem. In this paper, an improved rain and snow removal method from single image is proposed by designing a guided L0 smoothing filter. The designed filter is inspired by the previous L0 gradient minimization. Then a coarse rain-free or snow-free image can be obtained with the proposed filter, and the final refined result is recovered by a further minimization operation depending on the observed image. Experimental results show that the proposed algorithm generates better or comparable outputs than the state-of-the-art algorithms in rain and snow removal task for single image.     

基于全变差的Bandelet变换域SAR图像相干斑抑制方法

根据SAR图像的特性,提出了一种基于全变差最小化的Bandelet变换域SAR图像相干斑抑制方法。该方法首先对SAR图像进行Bandelet变换,得到图像的Bandelet系数和几何流,然后采用主成分分析法提取图像的Bandelet系数和几何流的主成分,利用全变差最小化的方法确定阈值,并通过循环递归运算处理重构后的图像。实验表明:该方法在抑制图像相干斑的同时较好地保留了几何结构信息,并去除了伪吉布斯现象。     

重复加权极小化MR图像模型的分裂Bregman方法重构

核磁共振(magnetic resonance,MR)图像重构的任务是基于小量的频域采样恢复出可供医学诊断的灰度图像.文中研究了各类MR变分重构模型,利用重复加权极小化能增强稀疏性的特性,并结合MR图像重构最有效的小波变分模型,提出了重复加权极小化MR图像重构模型.并借助最新的正则化技术—分裂Bregman方法对模型进行了求解,得到了相应的迭代算法,分析了算法的收敛性.仿真数值实验验证了文中的模型及算法的有效性.     

Reweighted minimization model for MR image reconstruction with split Bregman method

Magnetic resonance(MR) image reconstruction is to get a practicable gray-scale image from few frequency domain coefficients.In this paper,different reweighted minimization models for MR image reconstruction are studied,and a novel model named reweighted wavelet+TV minimization model is proposed.By using split Bregman method,an iteration minimization algorithm for solving this new model is obtained,and its convergence is established.Numerical simulations show that the proposed model and its algorithm are feasible and highly efficient.     

图像差与加权核范数最小化的压缩图像融合

现有的图像融合算法存在非线性操作产生的噪声干扰和空间复杂度高等问题,使得融合图像易失真和丢失信息。一些学者提出的压缩感知图像融合算法能有效改善这一问题,但大多忽略了图像矩阵的低秩性,往往会降低融合质量。由此,将压缩感知融合技术与低秩矩阵逼近方法相结合,提出基于信息论图像差与自适应加权核范数最小化的图像融合算法。该算法由3个阶段组成。首先,将2幅源图像通过小波稀疏基稀疏化,并利用结构随机矩阵压缩采样,得到测量输出矩阵。然后,将测量输出矩阵进行分块,再利用图像差融合算法得到融合后的测量输出矩阵块。最后,利用自适应加权核范数最小化优化得到的块权重,通过正交匹配追踪法重建融合图像。实验结果表明了该算法的有效性和普适性,并且在多种评价指标上优于其他融合算法。     

基于图像重构的MNIST对抗样本防御算法

随着深度学习的应用普及,其安全问题越来越受重视,对抗样本是在原有图像中添加较小的扰动,即可造成深度学习模型对图像进行错误分类,这严重影响深度学习技术的发展.针对该问题,分析现有对抗样本的攻击形式和危害,由于现有防御算法存在缺点,提出一种基于图像重构的对抗样本防御方法,以达到有效防御对抗样本的目的 .该防御方法以MNIS...     

一种改进的柱面全景视图生成方法

研究了相机存在绕光轴转动的情况下柱面全景图的生成，首先对柱面图像进行傅立叶变换，根据图像的傅立叶谱来估算出图像间的初始平移和旋转运动参数，再利用Levenberg-Marquardt非线性最小化方法逐步求精各运动参数，最后根据计算出的运动参数进行图像拼合获得柱面全景视图，实验结果表明该算法可以获得比较理想的拼接效果．     

基于带权核范数最小化和混合高斯模型的去噪模型

非局部自相似性（NSS）先验在图像恢复中发挥重要作用,如何充分利用这一先验提高图像恢复性能仍值得深入研究,提出一种基于带权核范数最小化和混合高斯模型的去噪模型。首先,采用混合高斯模型（GMM）对无噪声的自然图像非局部自相似图像块进行训练,再用训练好的混合高斯模型指导退化的图像产生非局部自相似图像块组;然后,结合带权的核范数最小化技术实现图像的去噪,并对模型的保真项进行一般性扩展,给出收敛的求解算法。仿真实验表明,所提方法与基于3D滤波的块匹配（BM3D）算法、同时稀疏编码学习（LSSC）算法和带权的核范数最小化（WNNM）模型相比,峰值信噪比（PSNR）提高0.11~0.49 dB。     

A Fast Fixed Point Algorithm for Total Variation Deblurring and Segmentation

In this paper, we propose a fast fixed point algorithm and apply it to total variation (TV) deblurring and segmentation. The TV-based models can be written in the form of a general minimization problem. The novel method is derived from the idea of establishing the relation between solutions of the general minimization problem and new variables, which can be obtained by a fixed point algorithm efficiently. Under gentle conditions it provides a platform to develop efficient numerical algorithms for various image processing tasks. We then specialize this fixed point methodology to the TV-based image deblurring and segmentation models, and the resulting algorithms are compared with the split Bregman method, which is a strong contender for the state-of-the-art algorithms. Numerical experiments demonstrate that the algorithm proposed here performs favorably.     

CUSTOM: A Calibration Region Recovery Approach for Highly Subsampled Dynamic Parallel Magnetic Resonance Imaging

We propose a recovery approach for highly subsampled dynamic parallel MRI image without auto-calibration signals (ACSs) or prior knowledge of coil sensitivity maps. By exploiting the between-frame redundancy of dynamic parallel MRI data, we first introduce a new low-rank matrix recovery-based model, termed as calibration using spatial–temporal matrix (CUSTOM), for ACSs recovery. The recovered ACSs from data are used for estimating coil sensitivity maps and further dynamic image reconstruction. The proposed non-convex and non-smooth minimization for the CUSTOM step is solved by a proximal alternating linearized minimization method, and we provide its convergence result for this specific minimization problem. Numerical experiments on several highly subsampled test data demonstrate that the proposed overall approach outperforms other state-of-the-art methods for calibrationless dynamic parallel MRI reconstruction.     

Efficient graph cut optimization using hybrid kernel functions for segmentation of FDG uptakes in fused PET/CT images

The purpose of this work is to segment the multi region Fluoro Deoxy Glucose radioactivity uptakes from fused Positron Emission Tomography / Computerized Tomography images automatically irrespective of their location in the body. Color image processing is performed to filter and enhance the saturation components of the images. The proposed method of graph cut image partitioning through kernel mapping of the image data is applied for the saturation equalized components of Red, Green, and Blue model images. Energy minimization of the objective function includes the data term minimization within each segmentation region and smoothening the regularization term preserving the boundary regions. Hybrid kernel functions are used for partitioning by graph-cut iterations and computation of region parameters through fixed-point computation. This method combines the performance of global and local kernel functions which makes the segmentation robust and accurate. The performance assessment is carried out for different views of fused Positron Emission Tomography / Computerized Tomography images, and are evaluated qualitatively, quantitatively, and comparatively. This method can be applied for the analysis of certain image features, diagnosis, and display purposes.     

基于自适应稀疏表达的并行磁共振重建方法

为精确地进行并行磁共振成像,文章利用字典学习的强大捕捉细节和稀疏开发能力,提出了一种基于自适应稀疏表达的重建方法。该方法将并行磁共振重建问题转化为最小化由字典学习和数据拟合项构成的目标函数,并采用了分而治之的方案求解未知变量。为验证其有效性,将该方法与目前主流的两种方法在人体实际磁共振数据上进行了测试。测试结果显示,文章提出的方法能在抑制图像噪声的同时较好地保存图像细节。     

图像修补的优化变换方法

采用整体变分(TV)模型修补图像,提出一种图像修补的优化变换方法.引入一个辅助变量,利用优化变换,将TV模型中单变量函数的优化问题转化为等效双变量函数的优化问题,并利用交替迭代最小化算法和Chambolle's投影算法求解模型.实验结果表明,与采用梯度下降法的TV模型算法相比,该方法的图像修补效率和修补效果较优.     

Data hiding in grayscale images by dynamic programming based on a human visual model

A new method for data hiding in grayscale images based on a human vision model with distortion-minimizing capabilities is proposed. Each of the eight bit planes of an input grayscale image is viewed as a binary image, into which message data are embedded horizontally. Two optimization techniques, namely, block pattern coding and dynamic programming, are proposed for image distortion minimization. Experimental results show good performs of the proposed method.     

Global optimization of wavelet-domain hidden Markov tree for image segmentation

This work presents a global energy minimization method for multiscale image segmentation using convex optimization theory. The construction of energy function is motivated by the intuition that the larger the entropy, the less a priori information one has on the value of the random variables. First, we represent the wavelet-domain hidden Markov tree (WHMT) model of the original image as a structured energy function, which is proved convex in marginal distributions. Next, we derive the maximum lower bound of the energy function through Lagrange dual transform for the purpose of incorporating marginal constraints into optimization. Finally, a modified belief propagation optimization algorithm is used to perform global energy minimization of the dual convex energy function. Experiments on real image segmentation problems demonstrate the superior performance of this new algorithm when compared with nonconvex ones.     

基于矩形子块法的快速全局立体匹配算法

由于计算量大,采用割图法(GC,GraphCuts)来最小化立体匹配的能量函数,计算速度慢,难以满足许多方面应用的要求。为了提高立体匹配能量函数最小化的速度,文章提出了一种新的算法:矩形子块割图法(RSRGC,RectangularSubregioningGraphCuts),即将大图像分割为小矩形子块,然后再在各小矩形子块上分别利用GC最小化能量函数。实验表明,采用该方法可以大大提高立体匹配的速度,而不影响立体匹配的效果。     

Optimal seamline detection in dynamic scenes via graph cuts for image mosaicking

In this paper, we present a novel method for creating a seamless mosaic from a set of geometrically aligned images captured from the scene with dynamic objects at different times. The artifacts caused by dynamic objects and geometric misalignments can be effectively concealed in our proposed seamline detection algorithm. In addition, we simultaneously compensate the image regions of dynamic objects based on the optimal seamline detection in the graph cuts energy minimization framework and create the mosaic with a relatively clean background. To ensure the high quality of the optimal seamline, the energy functions adopted in graph cuts combine the pixel-level similarities of image characteristics, including intensity and gradient, and the texture complexity. To successfully compensate the image regions covered by dynamic objects for creating a mosaic with a relatively clean background, we initially detect them in overlap regions between images based on pixel-level and region-level similarities, then refine them based on segments, and determine their image source in probability based on contour matching. We finally integrate all of these into the energy minimization framework to detect optimal seamlines. Experimental results on different dynamic scenes demonstrate that our proposed method is capable of generating high-quality mosaics with relatively clean backgrounds based on the detected optimal seamlines.     

Hybrid genetic optimization and statistical model based approachfor the classification of shadow shapes in sonar imagery

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process: 1) the detection problem of a region of interest in the input image is stated as the minimization of a cost function; and 2) the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique and two stochastic relaxation methods: simulated annealing and a hybrid genetic algorithm. This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results