基于稀疏表示的多正则优化图像复原

由于单正则化图像复原算法所利用的先验信息有限,影响了复原图像的质量。为克服此类算法的不足,融入更多的先验信息,改善图像复原的效果。在稀疏表示的理论框架下,提出了一种多正则优化图像复原算法。该算法将图像复原表示为含多正则项的全局优化问题,为有效处理这一复杂的图像复原问题,采用交替优化策略并借助变量分裂将其分解为若干优化子问题。其中,[uj+1]子问题可微,可直接得到其解析解。不可微的[wj+1]和[vj+1]子问题,则通过邻近映射求解。实验过程中对三种不同类型的退化图像进行了复原,所得结果验证了该算法的有效性。与FISTA（Fast Iterative Shrinkage-Thresholding Algorithm）和Split Bregman等单正则化图像复原算法相比,所提算法的复原效果和时间性能更优。     

基于双密度双树复小波变换多字典的人脸特征稀疏分类方法

基于超完备字典的人脸稀疏表示方法的难点是其字典构成。针对此问题,首先采用双密度双树复小波变换(DD-DT CWT)提取人脸图像不同尺度的高频子带,然后根据能量平均分布最大原则选择能量较大的部分子带构成对应尺度的超完备字典。同时,将测试样本相应的人脸DD-DT CWT子带特征看成超完备字典中原子的线性组合,并组合多字典上的稀疏表示进行识别。在AR人脸图像库上进行了实验,结果表明该方法是一种有效的人脸特征表示及分类方法。     

A robust facial image super-resolution model via mirror-patch based neighbor representation

Multimedia Tools and Applications - Many patch-based facial image super-resolution (or hallucination) techniques have been proposed in literature but all of them fail in the presence of...     

Learning group-based dictionaries for discriminative image representation

Dictionary learning is a critical issue for achieving discriminative image representation in many computer vision tasks such as object detection and image classification. In this paper, a new algorithm is developed for learning discriminative group-based dictionaries, where the inter-concept (category) visual correlations are leveraged to enhance both the reconstruction quality and the discrimination power of the group-based discriminative dictionaries. A visual concept network is first constructed for determining the groups of visually similar object classes and image concepts automatically. For each group of such visually similar object classes and image concepts, a group-based dictionary is learned for achieving discriminative image representation. A structural learning approach is developed to take advantage of our group-based discriminative dictionaries for classifier training and image classification. The effectiveness and the discrimination power of our group-based discriminative dictionaries have been evaluated on multiple popular visual benchmarks.     

基于稀疏表示的SAR图像压缩方法研究

基于过完备字典的图像稀疏表示是一种新的图像表示理论,利用过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。采用基于过完备字典稀疏表示的方法实现SAR图像的压缩。为了得到表示图像所需要的信息,只需要存储稀疏分解的系数极其对应的坐标,实现压缩的目的。采用K-SVD算法实现过完备字典的构造。K-SVD算法是一种基于学习的算法,由于训练样本全部来自于图像本身,因此字典能够更好地逼近图像本身的结构,实现稀疏表示。仿真表明对于SAR图像的压缩,算法是有效的,并且优于基于DCT的Jpeg算法和基于小波变换的EZW和SPIHT算法。     

Image super-resolution via two coupled dictionaries and sparse representation

In image processing, the super-resolution (SR) technique has played an important role to perform high-resolution (HR) images from the acquired low-resolution (LR) images. In this paper, a novel technique is proposed that can generate a SR image from a single LR input image. Designed framework can be used in images of different kinds. To reconstruct a HR image, it is necessary to perform an intermediate step, which consists of an initial interpolation; next, the features are extracted from this initial image via convolution operation. Then, the principal component analysis (PCA) is used to reduce information redundancy after features extraction step. Non-overlapping blocks are extracted, and for each block, the sparse representation is performed, which it is later used to recover the HR image. Using the quality objective criteria and subjective visual perception, the proposed technique has been evaluated demonstrating their competitive performance in comparison with state-of-the-art methods.     

Multi-polarimetric SAR image compression based on sparse representation

The use of sparse representation in signal and image processing has gradually increased over the past few years.Obtaining an over-complete dictionary from a set of signals allows us to represent these signals as a sparse linear combination of dictionary atoms.By considering the relativity among the multi-polarimetric synthetic aperture radar(SAR)images,a new compression scheme for multi-polarimetric SAR image based sparse representation is proposed.The multilevel dictionary is learned iteratively in the 9/7 wavelet domain using a single channel SAR image,and the other channels are compressed by sparse approximation,also in the 9/7 wavelet domain,followed by entropy coding of the sparse coefficients.The experimental results are compared with two state-of-the-art compression methods:SPIHT(set partitioning in hierarchical trees)and JPEG2000.Because of the efficiency of the coding scheme,our method outperforms both SPIHT and JPEG2000 in terms of peak signal-to-noise ratio(PSNR)and edge preservation index(EPI).     

Sparse media image restoration based on collaborative low rank representation

Multimedia Tools and Applications - Visual quality for image is a important problem that limits the performance in media big data analysis. To address this problem, we proposed a novel media image...     

A probabilistic model for image representation via multiple patterns

For image analysis, an important extension to principal component analysis (PCA) is to treat an image as multiple samples, which helps alleviate the small sample size problem. Various schemes of transforming an image to multiple samples have been proposed. Although having been shown effective in practice, the schemes are mainly based on heuristics and experience.In this paper, we propose a probabilistic PCA model, in which we explicitly represent the transformation scheme and incorporate the scheme as a stochastic component of the model. Therefore fitting the model automatically learns the transformation. Moreover, the learned model allows us to distinguish regions that can be well described by the PCA model from those that need further treatment. Experiments on synthetic images and face data sets demonstrate the properties and utility of the proposed model.     

Sparse representation based single image super-resolution with low-rank constraint and nonlocal self-similarity

Single image super-resolution reconstruction (SISR) plays an important role in many computer vision applications. It aims to estimate a high-resolution image from an input low-resolution image. In existing reconstruction methods, the nonlocal self-similarity based sparse representation methods exhibit good performance. However, for this kind of methods, due to the independent coding process of each image patch to be encoded, the global similarity information among all similar image patches in whole image is lost in reconstruction. As a result, similar image patches may be encoded as totally different code coefficients. Considering that the low-rank constraint is better at capturing the global similarity information, we propose a new sparse representation model, which concerns the low-rank constraint and the nonlocal self-similarity in the sparse representation model simultaneously, to preserve such global similarity information. The linearized alternating direction method with adaptive penalty is introduced to effectively solve the proposed model. Extensive experimental results demonstrate that the proposed model achieves convincing improvement over many state-of-the-art SISR models. Moreover, these good results also demonstrate the effectiveness of the proposed model in preserving the global similarity information.     

带PCA卷积的稀疏表示图像分类算法

针对不同卷积核可以提取不同的图像特征,而卷积核的训练比较困难这一问题,提出一种带主成分分析（PCA）卷积的稀疏表示分类算法。先对训练样本集做分片去均值化处理,然后直接应用PCA算法提取所有分片的前K个特征向量作为卷积核,再用这些卷积核对原始图像进行卷积操作;并提出一种自动加权策略,对卷积处理后得到的K个特征图像进行加权叠加操作;最后对特征图像进行分块直方图统计稀疏化,并应用稀疏表示分类算法进行分类。在公共人脸数据集AR、CMU Multi-PIE、ORL以及数字手写体数据集MNIST上与常用分类算法进行对比实验,实验结果表明,带PCA卷积的稀疏表示分类算法具有更高的分类准确率。     

Adaptive face representation via class-specific and intra-class variation dictionaries for recognition

Face recognition has attracted extensive interests due to its wide applications. However, there are many challenges in the real world scenario. For example, relatively few samples are available for training. Face images collected from surveillance cameras may consist of complex variations (e.g. illumination, expression, occlusion and pose). To address these challenges, in this paper we propose learning class-specific and intra-class variation dictionaries separately. Specifically, we first develop a discriminative class-specific dictionary amplifying the differences between training classes. We impose a constraint on sparse coefficients, which guarantees the sparse representation coefficients having small within-class scatter and large between-class scatter. Moreover, we introduce a new intra-class variation dictionary based on the assumption that similar variations from different classes may share some common features. The intra-class variation dictionary not only captures the inner-relationship of variations, but also addresses the limitation of the manually designed dictionaries that are person-specific. Finally, we apply the combined dictionary to adaptively represent face images. Experiments conducted on the AR, CMU-PIE, FERET and Extended Yale B databases show the effectiveness of the proposed method.     

基于变异系数的分形图像压缩稀疏编码算法

针对分形图像编码算法复杂度高、编码时间冗长的问题,提出正交稀疏编码和纹理特征提取表示图像块的方法.首先,灰度级的正交稀疏变换提高了图像的重建质量和解码时间.其次,相关系数矩阵度量范围块和域块之间的变异系数特征降低了冗余度和编码时间.仿真实验结果显示,该方法与传统的分形图像编码算法相比,图像重建质量更好,编码速度更快.     

Gabor特征集结合判别式字典学习的稀疏表示图像识别

稀疏编码中字典的选择无论对图像重建还是模式分类都有重要影响,为此提出Gabor特征集结合判别式字典学习的稀疏表示图像识别算法.考虑到Gabor局部特征对光照、表情和姿态等变化的鲁棒性,首先提取图像对应不同方向、不同尺度的多个Gabor特征;然后将降维的增广Gabor特征矩阵作为初始特征字典,通过对该字典的学习得到字典原子对应类别标签的新结构化字典,新字典中特定类的子字典对相关的类具有好的表示能力,同时应用Fisher判别约束编码系数,使它们具有小的类内散度和大的类间散度;最后同时用具有判别性的重构误差和编码系数来进行模式分类.基于3个数据库的实验结果表明本文方法具有可行性和有效性.     

面向图像集分类的切空间稀疏表示算法

目的 在基于图像集的分类任务中,用SPD （symmetric positive definite）矩阵描述图像集,并考虑所得到的黎曼流形,已被证明对许多分类任务有较好的效果。但是,已有的经典分类算法大多应用于欧氏空间,无法直接应用于黎曼空间。为了将欧氏空间的分类方法应用于解决图像集的分类,综合考虑SPD流形的LEM （Log-Euclidean metric）度量和欧氏空间分类算法的特性,实现基于图像集的分类任务。方法 通过矩阵的对数映射将SPD流形上的样本点映射到切空间中,切空间中的样本点与图像集是一一对应的关系,此时,再将切空间中的样本点作为欧氏空间中稀疏表示分类算法的输入以实现图像集的分类任务。但是切空间样本的形式为对称矩阵,且维度较大,包含一定冗余信息,为了提高算法的性能和运行效率,使用NYSTRÖM METHOD和（2D）²PCA （two-directional two-dimensional PCA）两种方法来获得包含图像集的主要信息且维度更低的数据表示形式。结果 在实验中,对人脸、物体和病毒细胞3种不同的对象进行分类,并且与一些用于图像集分类的经典算法进行对比。实现结果表明,本文算法不仅具有较高的识别率,而且标准差也相对较小。在人脸数据集上,本文算法的识别率可以达到78.26%,比其他算法高出10%左右,同时,具有最小的标准差2.71。在病毒数细胞据集上,本文算法的识别率可以达到58.67%,在所有的方法中识别率最高。在物体识别的任务中,本文算法的识别率可以达到96.25%,标准差为2.12。结论 实验结果表明,与一些经典的基于图像集的分类算法对比,本文算法的识别率有较大的提高且具有较小的标准差,对多种数据集有较强的泛化能力,这充分说明了本文算法可以广泛应用于解决基于图像集的分类任务。但是,本文是通过（2D）²PCA和NYSTRÖM METHOD对切空间中样本进行降维来获得更低维度的样本,以提高算法的运行速度和性能。如何直接构建维度更低,且具有判别性的SPD流形将是下一步的研究重点。     

Blind image deblurring via gradient orientation-based clustered coupled sparse dictionaries

Pattern Analysis and Applications - In this paper, we proposed a novel sparse representation-based blind image deblurring algorithm, which exploits the benefits of coupled sparse dictionary, and...     

基于SPIHT算法的改进ROI图像编码

将分层树集合划分算法(Set Partitioning in Hierarchcal Trees, SPIHT)引入到感兴趣区域(Region of Interest, ROI)编码中。对前n个高比特平面进行编码时,将ROI区域与背景无区分进行编码。对n个高比特平面之后的平面进行编码时,将背景区域进行清零操作,ROI区域继续用SPIHT算法进行编码。此算法的优点：位平面数减少;整个图像的基本信息能够优先被恢复出来。在解码端,依靠原有的SPIHT解码程序就可以将其解码出来。可用于现有的SPIHT算法应用中,并且能够很好地恢复感兴趣的图像。     

Hierarchical image representation for compression,filtering and normalization

Applications of pyramid data structure to image compression and image filtering are described. The compression scheme fits visual perception models, and filtering is computationally faster than filtering in the frequency domain. Image normalization is also possible.