基于可区分性字典学习模型的极化SAR图像分类

极化SAR图像分类是一个高维非线性映射问题,稀疏表示(CS)对于解决此类问题具有很大潜力。字典学习在基于CS的分类中起到重要作用。本文提出了一种新的字典学习模型,用于增强字典的区分能力,使其更适合极化SAR图像分类。提出的模型根据字典中两类子字典在分类中的作用对其相应的表达系数施加不同的稀疏约束。为使共同子字典能够抓住所有类共享的特征,对其相应系数施加稀疏约束,为使类专属子字典能够抓住类内独享的局部和全局结构特征,对其相应系数同时施加稀疏和低秩约束。由于共同子字典表达所有类共享的特征,我们以测试样本在类专属子字典上的重建误差作为准则进行分类。本文在AIRSAR的Flevoland数据集上对此算法进行验证,实验结果验证了算法的有效性。      

基于空间约束多特征联合稀疏编码的遥感图像标注方法研究

针对稀疏表示分类器不能较好地适应多特征框架的问题,该文提出一种空间约束多特征联合稀疏编码模型,并以此实现遥感影像的自动标注。该方法利用l1,2混合范数正则化多特征编码系数,约束编码系数共享相同的稀疏模式,在保持多特征关联的同时,又不添加过于严格的约束。同时,将字典学习技术扩展到多特征框架中,通过约束字典更新的变换矩阵,解决了字典学习过程丢失多特征关联的问题。另外,针对遥感影像中的空间关系常常被忽略或者利用不充分的不足,还提出了将空间一致性与多特征联合稀疏编码相结合的分类准则,提高了标注性能。在遥感公开数据集与大尺寸卫星影像上的实验证明了该方法的有效性。     

基于核Fisher判别字典学习的稀疏表示分类

在基于稀疏表示分类的模式识别中,字典学习(DL) 可以为稀疏表示获得更为精简的数据表示。最近的基于Fisher判别的字典学习(FDDL)可以学 习到更加判别的稀疏字典,使得稀疏表示分类具有很强的识别性能。核空间变换可以学习到 非线性结构信息,这对判别分类非常有用。为了充分利用 核空间特性以学习更加判别的稀疏字典来提升最终的识别性能,在FDDL的基础上,提出了两 种核化的稀疏表示DL方法。首先原始训练数据被投影到高维核空间,进行基于Fisher 判别的核稀 疏表示DLFDKDL;其次在稀疏系数上附加核Fisher约束,进行基于核Fisher判别的核稀疏表 示DL(KFDKDL),使得所学习的字典具有更强的判别能力。在多个公开的图像数据库上的稀疏 表示分类实验结果验证了所提出的FDKDL和KFDKDL方法的有效性。     

基于图像稀疏表示的红外小目标检测算法

基于超完备字典的图像稀疏表示是一种新的图像表示理论,利用超完备字典的冗余性可以有效地捕捉图像的各种结构特征,从而实现图像的有效表示.针对红外小目标检测问题,提出了一种基于图像稀疏表示的检测方法,该方法采用二维高斯模型生成样本图像,继而构造超完备目标字典,然后依次提取测试图像的图像子块并计算其在超完备字典中的表示系数,背...     

Supervised within-class-similar discriminative dictionary learning for face recognition

The current study puts forward a supervised within-class-similar discriminative dictionary learning (SCDDL) algorithm for face recognition. Some popular discriminative dictionary learning schemes for recognition tasks always incorporate the linear classification error term into the objective function or make some discriminative restrictions on representation coefficients. In the presented SCDDL algorithm, we propose to directly restrict the representation coefficients to be similar within the same class and simultaneously include the linear classification error term in the supervised dictionary learning scheme to derive a more discriminative dictionary for face recognition. The experimental results on three large well-known face databases suggest that our approach can enhance the fisher ratio of representation coefficients when compared with several dictionary learning algorithms that incorporate linear classifiers. In addition, the learned discriminative dictionary, the large fisher ratio of representation coefficients and the simultaneously learned classifier can improve the recognition rate compared with some state-of-the-art dictionary learning algorithms.     

An image denoising algorithm for mixed noise combining nonlocal means filter and sparse representation technique

Nonlocal means (NLM) filtering or sparse representation based denoising method has obtained a remarkable denoising performance. In order to integrate the advantages of two methods into a unified framework, we propose an image denoising algorithm through skillfully combining NLM and sparse representation technique to remove Gaussian noise mixed with random-valued impulse noise. In the non-Gaussian circumstance, we propose a customized blockwise NLM (CBNLM) filter to generate an initial denoised image. Based on it, we classify the different noisy pixels according to the three-sigma rule. Besides, an overcomplete dictionary is trained on the initial denoised image. Then, a complementary sparse coding technique is used to find the sparse vector for each input noisy patch over the overcomplete dictionary. Through solving a more reasonable variational denoising model, we can reconstruct the clean image. Experimental results verify that our proposed algorithm can obtain the best denoising performance, compared with some typical methods.     

稀疏表达的自适应遥感图像融合算法

本文提出一种基于稀疏表达的图像融合算法。该算法利用稀疏系数中的非零元素所对应的基向量作为图像特征,首先分离相同基向量和相异基向量,然后采用加权求和算法合并相对应的稀疏系数,并重构得到融合图像。该算法对相同特征和相异特征分别进行融合,克服了融合图像中相异特征清晰度下降的问题。并且由于稀疏表达具有很好的去噪功能,本文算法也可以同时进行图像融合和去噪。通过与4种流行的融合算法比较,本文算法得到较好的视觉效果。      

基于多重核的稀疏表示分类

稀疏表示分类（SRC）及核方法在模式识别的很多问题中都得到了成功的运用.为了提高其分类精度,提出多重核稀疏表示及其分类（MKSRC）方法.提出一种快速求解稀疏系数的优化迭代方法并给出了其收敛到全局最优解的证明.对于多重核的权重给出了两种自动更新方式并进行了分析与比较.在不同的人脸图像库上的分类实验显示了所提出的多重核稀疏表示分类的优越性.     

Object tracking using discriminative sparse appearance model

Object tracking based on sparse representation formulates tracking as searching the candidate with minimal reconstruction error in target template subspace. The key problem lies in modeling the target robustly to vary appearances. The appearance model in most sparsity-based trackers has two main problems. The first is that global structural information and local features are insufficiently combined because the appearance is modeled separately by holistic and local sparse representations. The second problem is that the discriminative information between the target and the background is not fully utilized because the background is rarely considered in modeling. In this study, we develop a robust visual tracking algorithm by modeling the target as a model for discriminative sparse appearance. A discriminative dictionary is trained from the local target patches and the background. The patches display the local features while their position distribution implies the global structure of the target. Thus, the learned dictionary can fully represent the target. The incorporation of the background into dictionary learning also enhances its discriminative capability. Upon modeling the target as a sparse coding histogram based on this learned dictionary, our tracker is embedded into a Bayesian state inference framework to locate a target. We also present a model update scheme in which the update rate is adjusted automatically. In conjunction with the update strategy, the proposed tracker can handle occlusion and alleviate drifting. Comparative results on challenging benchmark image sequences show that the tracking method performs favorably against several state-of-the-art algorithms.     

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％。     

A video semantic detection method based on locality-sensitive discriminant sparse representation and weighted KNN

Video semantic detection has been one research hotspot in the field of human-computer interaction. In video features-oriented sparse representation, the features from the same category video could not achieve similar coding results. To address this, the Locality-Sensitive Discriminant Sparse Representation (LSDSR) is developed, in order that the video samples belonging to the same video category are encoded as similar sparse codes which make them have better category discrimination. In the LSDSR, a discriminative loss function based on sparse coefficients is imposed on the locality-sensitive sparse representation, which makes the optimized dictionary for sparse representation be discriminative. The LSDSR for video features enhances the power of semantic discrimination to optimize the dictionary and build the better discriminant sparse model. More so, to further improve the accuracy of video semantic detection after sparse representation, a weighted K-Nearest Neighbor (KNN) classification method with the loss function that integrates reconstruction error and discrimination for the sparse representation is adopted to detect video semantic concepts. The proposed methods are evaluated on the related video databases in comparison with existing sparse representation methods. The experimental results show that the proposed methods significantly enhance the power of discrimination of video features, and consequently improve the accuracy of video semantic concept detection.     

Geometry-based distributed scene representation with omnidirectional vision sensors

This paper addresses the problem of efficient representation of scenes captured by distributed omnidirectional vision sensors. We propose a novel geometric model to describe the correlation between different views of a 3-D scene. We first approximate the camera images by sparse expansions over a dictionary of geometric atoms. Since the most important visual features are likely to be equivalently dominant in images from multiple cameras, we model the correlation between corresponding features in different views by local geometric transforms. For the particular case of omnidirectional images, we define the multiview transforms between corresponding features based on shape and epipolar geometry constraints. We apply this geometric framework in the design of a distributed coding scheme with side information, which builds an efficient representation of the scene without communication between cameras. The Wyner-Ziv encoder partitions the dictionary into cosets of dissimilar atoms with respect to shape and position in the image. The joint decoder then determines pairwise correspondences between atoms in the reference image and atoms in the cosets of the Wyner-Ziv image in order to identify the most likely atoms to decode under epipolar geometry constraints. Experiments demonstrate that the proposed method leads to reliable estimation of the geometric transforms between views. In particular, the distributed coding scheme offers similar rate-distortion performance as joint encoding at low bit rate and outperforms methods based on independent decoding of the different images.     

多极化SAR图像联合稀疏去噪

给出了一种基于联合稀疏表示的多极化合成孔径雷达(SAR)图像滤波算法。首先,利用三个极化通道(HH、HV、VV)的部分SAR图像数据进行字典联合训练;然后,对极化SAR的三个通道图像构建联合稀疏描述模型;最后,采用正交匹配追踪算法求解联合稀疏系数,重构每个通道的图像。文中采用美国AIRSAR实测半月湾数据进行实验,并与每个通道图像单独稀疏去噪再合成的功率图像结果进行比较,结果表明:该算法不仅对图像的斑点噪声抑制效果明显,而且边缘特性和强散射点目标幅值特征保持效果良好。     

基于代理函数优化的稀疏性字典学习

稀疏性字典学习是指对在某个已知的基字典上具有稀疏表示的字典的学习.论文利用块松弛思想,将稀疏性字典学习问题转化为字典和系数的分别优化问题,利用代理函数优化方法分别对固定字典和固定系数情况下的目标函数进行优化处理,得到固定字典情况下的系数更新算法和固定系数情况下的字典更新算法,进而得到稀疏性字典学习算法.理论分析说明了本...     

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.     

基于正则化稀疏表示的图像超分辨率算法

为了从单幅低分辨率(LR)图像恢复出高分辨率(H R)图像,提出了一种应用正则化稀疏表示和基于机器学习 的超分辨率(SR)图像恢复算法。构造了一种基于稀疏表示的SR凸变模型,为了提高 恢复效果,针对模型 提出了两种稀疏正则化约束条件,一是将分类效果更好的图表拉普拉斯作为正则化约束条件 ,从而找到与 输入LR图像块在结构上最接近的学习样本;另一种是针对冗余的学习样本进行约 束,保证了图像边 缘的锐利。将输入的每一块LR图像应用正则化稀疏表示,经过学习得到与之对应的HR图像块 , 最终得到整幅HR图像。试验结果表明,算法恢复出的HR图像峰值信噪比(PSNR )值较双三次插值算法最高提升约2dB,主观目视清晰、边缘锐利。     

可见光与红外图像组K-SVD融合方法

传统稀疏表示融合方法,以图像块进行字典训练和稀疏分解,由于没有考虑图像块之间的内在联系,易造成字典原子表征图像特征能力不足、稀疏系数不准确,导致图像融合效果不好.为此,本文提出可见光与红外图像组K-SVD(K-means singular value decomposition)融合方法,利用图像的非局部相似性,将相似...     

彩色图像去马赛克的非局部稀疏表示方法

目前,大部分彩色去马赛克（Color DeMosaicking,CDM）算法仅利用了局部的空间和光谱相关性,容易导致CDM复原图像边缘模糊以及细小结构丢失.当图像中出现周期性细小结构时,这些局部方法容易产生诸如锯齿、栅格等失真现象.针对这些问题,我们将字典学习和稀疏编码统一到一个变分框架中,提出了非局部自适应稀疏表示模型.通过非局部相似块聚类自适应地在线学习字典.利用局部和非局部的冗余信息对稀疏编码进行约束,强制稀疏编码靠近其非局部均值以减少编码误差.为了有效抑制服从重尾分布的CDM误差,设计了基于l₁范数的数据项.最后,联合交替最小化方法和算子分裂技巧对模型进行有效求解.实验结果验证了本文模型与数值算法的有效性.     

稀疏性正则化的图像泊松去噪算法

去除医学、天文图像中的泊松噪声是一个重要问题,基于图像在过完备字典下的稀疏表示,在BayesianMAP框架下建立了稀疏性正则化的图像泊松去噪凸变分模型,采用负log的泊松似然函数作为模型的数据保真项,模型中非光滑的正则项约束图像表示系数的稀疏性,并附加非负件约束,保证去噪图像的非负性.基于分裂Bregman方法,提出...     

基于块分类稀疏表示的图像融合算法

针对基于单一字典训练稀疏表示的图像融合算法忽略图像局部特征的问题,提出了基于块分类稀疏表示的图像融合算法。此算法是根据图像局部特征的差异将图像块分为平滑、边缘和纹理三种结构类型,对边缘和纹理结构分别训练出各自的冗余字典。平滑结构利用算术平均法进行融合,边缘和纹理结构由对应字典利用稀疏表示算法进行融合,并对边缘结构稀疏表示中的残余量进行小波变换融合。实验结果证明,该算法相对于单一字典稀疏表示算法,在融合图像的主观评价和客观评价指标上都有显著改进,并且算法速度也有提高。