可见光与红外图像结构组双稀疏融合方法研究

传统的可见光与红外稀疏表示融合方法,采用图像块构造解析字典或者学习字典,利用字典的原子表征图像的显著特征。这类方法存在两个问题,一是没有考虑图像块与块之间的联系,二是字典的适应能力不够并且复杂度高。针对这两个问题,本文提出可见光与红外图像结构组双稀疏融合方法。该方法首先利用图像的非局部相似性,将图像块构建成图像相似结构组,然后对图像相似结构组进行字典训练,采用双稀疏分解模型,有效结合解析字典和学习字典的优势,降低了字典训练的复杂度,得到的结构字典更加灵活,适应性提高。该方法能够有效提高红外与可见光融合图像的视觉效果,经对比实验分析,在主观和客观评价上都优于传统的稀疏表示融合方法。     

基于稀疏优化的图像压缩感知重建算法

在信号的稀疏表示方法中,传统的基于变换基的稀疏逼近不能自适应性地提取图像的纹理特征,而基于过完备字典的稀疏逼近算法复杂度过高.针对该问题,文章提出了一种基于小波变换稀疏字典优化的图像稀疏表示方法.该算法在图像小波变换的基础上构建图像过完备字典,利用同一场景图像的小波变换在纹理上具有内部和外部相似的属性,对过完备字典进行灰色关联度的分类,有效提高了图像表示的稀疏性.将该新算法应用于图像信号进行稀疏表示,以及基于压缩感知理论的图像采样和重建实验,结果表明新算法总体上提升了重建图像的峰值信噪比与结构相似度,并能有效缩短图像重建时间.     

Enhancing inverse halftoning via coupled dictionary training

Inverse halftoning is a challenging problem in image processing. Traditionally, this operation is known to introduce visible distortions into reconstructed images. This paper presents a learning-based method that performs a quality enhancement procedure on images reconstructed using inverse halftoning algorithms. The proposed method is implemented using a coupled dictionary learning algorithm, which is based on a patchwise sparse representation. Specifically, the training is performed using image pairs composed by images restored using an inverse halftoning algorithm and their corresponding originals. The learning model, which is based on a sparse representation of these images, is used to construct two dictionaries. One of these dictionaries represents the original images and the other dictionary represents the distorted images. Using these dictionaries, the method generates images with a smaller number of distortions than what is produced by regular inverse halftone algorithms. Experimental results show that images generated by the proposed method have a high quality, with less chromatic aberrations, blur, and white noise distortions.     

自适应聚类的全耦合稀疏学习算法

在图像处理领域,基于稀疏表示理论的图像超分辨力算法、高低分辨力字典与稀疏编码之间的映射关系是其中的2个关键环节。由于丰富多样的图像类型,单一字典并不能很好地表示图像。而在稀疏编码之间的映射关系上,严格相等的约束关系也限制了图像重建的效果。针对上述两个方面,采用包容性更强的多个字典与约束条件更为宽松的全耦合稀疏关系进行图像的超分辨力重建。在图像非局部自相似性的基础上,进行多次自适应聚类;挑选出最优的聚类,通过全耦合稀疏学习的图像超分辨力算法,得到多个字典;最后,对输入的低分辨力图像进行分类重建,得到高分辨力图片。实验结果表明,在图像Leaves,Barbara,Room上,本文的聚类算法比原全耦合稀疏学习算法在峰值信噪比(PSNR)上分别提升了0.51 dB,0.21 dB,0.15 dB。     

Nonparametric Bayesian dictionary learning algorithm based on structural similarity

Though nonparametric Bayesian methods possesses significant superiority with respect to traditional comprehensive dictionary learning methods,there is room for improvement of this method as it needs more consideration over the structural similarity and variability of images.To solve this problem,a nonparametric Bayesian dictionary learning algorithm based on structural similarity was proposed.The algorithm improved the structural representing ability of dictionaries by clustering images according to their non-local structural similarity and introducing block structure into sparse representing of images.Denoising and compressed sensing experiments showed that the proposed algorithm performs better than several current popular unsupervised dictionary learning algorithms.     

基于稀疏编码的鲁棒型人脸超分辨率重建

为了减少人脸超分图像的边缘伪影和图像噪点,利用基于稀疏编码的单幅图像超分辨率重建算法,在字典学习阶段,结合L1范数引入在线字典学习方法,使字典根据当前输入图像块和上次迭代生成的字典逐列更新,得到更加精确的超完备字典对,用于图像重建.实验中进行的仿真结果表明,改进算法超分结果的峰值信噪比(PSNR)和结构相似性(SSIM)比同类型的稀疏编码超分法(SCSR)和应用在线字典学习算法的超分方法(ODLSR)均有较大幅度提升,比后者平均提升0.72 dB和0.0187.同时,视觉上有效地消除了边缘伪影,且在处理含噪人脸图像时,具备更强的去噪能力和更好的鲁棒性.     

基于小波字典稀疏表示的SAR图像目标识别

针对稀疏表示识别算法在图像域构造冗余字典时过分依赖预处理及原子维数较大的问题,提出基于小波字典的 SAR图像稀疏表示识别算法。首先采用二维离散小波变换将原始图像变换到小波域,建立小波域 SAR图像特征模型,得出小波域低频成分可充分表征目标类别信息的结论。然后取小波域低频成分进行2DPCA特征抽取构造小波字典,最后由改进 OMP 算法稀疏分解系数得到识别结果。SAR MSTAR数据的实验结果表明,在无预处理的情况下识别率高达99％,并且在含噪比10％的情况下识别率仍达96％。     

基于耦合特征空间下改进字典学习的图像超分辨率重建

针对目前基于字典学习的图像超分辨率重建效果欠佳或字典训练时间过长的问题,本文提出了一种耦合特征空间下改进字典学习的图像超分辨率重建算法.该算法首先利用高斯混合模型聚类算法对训练图像块进行聚类,然后使用更改字典更新方式的改进KSVD字典学习算法来快速获得高、低分辨率特征空间下字典对和映射矩阵.重建时根据测试样本与各个类别的似然概率自适应地选择最匹配的字典对和映射矩阵进行高分辨率重建.最后利用图像非局部相似性,将其与迭代反向投影算法相结合对重建后的图像进行后处理获得最佳重建效果.实验结果表明了本文方法的有效性.     

A novel predual dictionary learning algorithm

Dictionary learning has been a hot topic fascinating many researchers in recent years. Most of existing methods have a common character that the sequences of learned dictionaries are simpler and simpler regularly by minimizing some cost function. This paper presents a novel predual dictionary learning (PDL) algorithm that updates dictionary via a simple gradient descent method after each inner minimization step of Predual Proximal Point Algorithm (PPPA), which was recently presented by Malgouyres and Zeng (2009) [F. Malgouyres, T. Zeng, A predual proximal point algorithm solving a non negative basis pursuit denoising model, Int. J. Comput. Vision 83 (3) (2009) 294-311]. We prove that the dictionary update strategy of the proposed method is different from the current ones because the learned dictionaries become more and more complex regularly. The experimental results on both synthetic data and real images consistently demonstrate that the proposed approach can efficiently remove the noise while maintaining high image quality and presents advantages over the classical dictionary learning algorithms MOD and K-SVD.     

Fast search for best representations in multitree dictionaries.

We address the best basis problem--or, more generally, the best representation problem: Given a signal, a dictionary of representations, and an additive cost function, the aim is to select the representation from the dictionary which minimizes the cost for the given signal. We develop a new framework of multitree dictionaries, which includes some previously proposed dictionaries as special cases. We show how to efficiently find the best representation in a multitree dictionary using a recursive tree-pruning algorithm. We illustrate our framework through several examples, including a novel block image coder, which significantly outperforms both the standard JPEG and quadtree-based methods and is comparable to embedded coders such as JPEG2000 and SPIHT.     

Image reconstruction with locally adaptive sparsity and nonlocal robust regularization

Sparse representation based modeling has been successfully used in many image-related inverse problems such as deblurring, super-resolution and compressive sensing. The heart of sparse representations lies on how to find a space (spanned by a dictionary of atoms) where the local image patch exhibits high sparsity and how to determine the image local sparsity. To identify the locally varying sparsity, it is necessary to locally adapt the dictionary learning process and the sparsity-regularization parameters. However, spatial adaptation alone runs into the risk of over-fitting the data because variation and invariance are two sides of the same coin. In this work, we propose two sets of complementary ideas for regularizing image reconstruction process: (1) the sparsity regularization parameters are locally estimated for each coefficient and updated along with adaptive learning of PCA-based dictionaries; (2) a nonlocal self-similarity constraint is introduced into the overall cost functional to improve the robustness of the model. An efficient alternative minimization algorithm is present to solve the proposed objective function and then an effective image reconstruction algorithm is presented. The experimental results on image deblurring, super-resolution and compressive sensing demonstrate that the proposed image reconstruct method outperforms many existing image reconstruction methods in both PSNR and visual quality assessment.     

特征聚类的局部敏感稀疏图像修复

针对图像修复过程中单一的字典迭代时间长、适应性差、修复效果不理想的缺点,提出了一种结合图像特征聚类与字典学习的改进的图像修复方式。首先破损的图像被分割成小块,并产生索引矩阵。然后使用控制核回归权值算法,对其进行图像聚类。通过对图像内在结构与未破损区域信息的挖掘,分割的图像块根据SKRW的相似性进行了分类。之后针对不同类型结构的图像,通过自适应局部明感字典学习的方式,获取每类字典的过完备字典。然后,通过构建自适应局部配适器,提高字典更新的收敛速度与稀疏字典的适应性。因为是通过多个字典匹配不同结构的图像,因此图像的稀疏表示更为准确。各个字典在达到收敛之前不断进行更新,而图像的稀疏因子也会随着改变。在对破损区域进行补丁更换之后,实现了对破损图像的修复。实验结果表明,该算法相较于目前的修复算法,视觉效果和客观评价上更好,且所需的修复时间更短。     

Sparse representation for color image restoration

Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.     

Structural image retrieval using automatic image annotation and region based inverted file

Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.     

一种基于超完备字典学习的图像去噪方法

 基于超完备字典的图像稀疏表示因其具有稀疏性、特征保持性、可分性等特点而被广泛应用于图像处理.本文提出一种超完备字典学习算法并应用于图像去噪.将字典学习等价于一个二次规划问题,并提出适合于大规模运算的投影梯度算法.学习所得字典能有效描述图像特征.基于此超完备学习字典,获得图像的稀疏表示,并恢复原始图像.实验结果表明,与小波类去噪方法相比,本文的学习算法能更好地去除图像噪声,保留图像细节信息,获得更高的PSNR值.     

一种基于多字典学习的图像分割模糊方法

本文提出一种基于多字典学习的图像分割模糊模型和算法.在模型中,结合多字典学习和模糊方法,考虑了分割区域内部的一致性和边界的正则性：一方面使用区域块均值和带有类标的结构字典重构图像块,利用重构误差和l2正则能量共同度量分割区域内部的一致性,该度量能够刻画图像不同区域的灰度信息和纹理模式;另一方面采用小波系数稀疏正则保持分割区域边界的几何结构.基于交替方向乘子法和字典学习方法给出新模型的快速求解算法.在该算法中,除了小波阈值,每一步都是显示表达式,因此简单易用.一系列实验结果验证了本文算法的有效性.     

Best basis search in lapped dictionaries

This paper proposes, analyzes, and illustrates several best basis search algorithms for dictionaries consisting of lapped orthogonal bases. It improves upon the best local cosine basis selection based on a dyadic tree , by considering larger dictionaries of bases. It is shown that this can result in sparser representations and approximate shift invariance. An algorithm that is strictly shift invariant is also provided. The experiments in this paper suggest that the new dictionaries can be advantageous for time-frequency analysis, compression, and noise removal. Accelerated versions of the basic algorithm are provided that explore various tradeoffs between computational efficiency and adaptability. It is shown that the proposed algorithms are in fact applicable to any finite dictionary comprised of lapped orthogonal bases. One such novel dictionary is proposed that constructs the best local cosine representation in the frequency domain, and it is shown that the new dictionary is better suited for representing certain types of signals.     

A Secure Watermarking Algorithm Based on Coupled Map Lattice

Based on the nonlinear theory, a secure watermarking algorithm using wavelet transform and coupled map lattice is presented. The chaos is sensitive to initial conditions and has a good non-relevant correlation property, but the finite precision effect limits its application in practical digital watermarking system. To overcome the undesirable short period of chaos mapping and improve the security level of watermarki‘ng, the hyper-chaotic sequence is adopted in this algorithm. The watermark is mixed with the hyper-chaotic sequence and embedded in the wavelet domain of the host image. Experimental results and analysis are given to demonstrate that the proposed watermarking algorithm is transparent, robust and secure.     

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.     

具有聚类结构相似性的非参数贝叶斯字典学习算法

利用图像结构信息是字典学习的难点,针对传统非参数贝叶斯算法对图像结构信息利用不充分,以及算法运行效率低下的问题,该文提出一种结构相似性聚类beta过程因子分析(SSC-BPFA)字典学习算法。该算法通过Markov随机场和分层Dirichlet过程实现对图像局部结构相似性和全局聚类差异性的兼顾,利用变分贝叶斯推断完成对概率模型的高效学习,在确保算法收敛性的同时具有聚类的自适应性。实验表明,相比目前非参数贝叶斯字典学习方面的主流算法,该文算法在图像去噪和插值修复应用中具有更高的表示精度、结构相似性测度和运行效率。