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.     

基于稀疏表达的遮挡目标跟踪算法

针对视觉跟踪中的目标遮挡问题,提出一种基于稀疏表达的视觉跟踪算法。采用稀疏表达方法描述跟踪目标,构造基于Gabor特征的目标词典和遮挡词典,通过l1范数最优化求解稀疏表达系数。在粒子滤波框架下跟踪目标,根据稀疏表达系数判断遮挡,并利用重构残差更新遮挡情况下的粒子权重。在目标模板更新时,通过引入可靠性评价来抑制模板漂移。实验结果表明,该算法能够有效地跟踪处于遮挡状态下的运动目标,并对目标姿态变化以及光照变化具有较好的鲁棒性。     

Predictive high-level feature representation based on dictionary learning

A much improved computational performance of visual recognition tasks can be achieved by representing raw input data (low-level) with high-level feature representation. In order to generate the high-level representation, a sparse coding is widely used. However, a major problem in traditional sparse coding is computational performance due to an ℓ₀/ℓ₁ optimization. Often, this process takes significant amount of time to find the corresponding coding coefficients. This paper proposed a new method to create a discriminative sparse coding that is more efficient to compute the coding coefficients with minimum computational effort. More specifically, a linear model of sparse coding prediction was introduced to estimate the coding coefficients. This is accomplished by computing the matrix-vector product. We named this proposed method as predictive sparse coding K-SVD algorithm (PSC–KSVD). The experimental results demonstrated that PSC–KSVD achieved promising classification results on well-known benchmark image databases. Furthermore, it outperformed the currently approaches in terms of computational time. Consequently, PSC–KDVD can be considered as a suitable method to apply in real-time classification problems especially with large databases.     

Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification

The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.     

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).     

Fractional-order Sparse Representation for Image Denoising

Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation (FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster. Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.      

Sparse coding for image classification base on spatial pyramid representation

Many efforts have been devoted to apply sparse coding for image classification with the aim of minimizing the reconstruction error and classification error. So far, the approaches have been proposed either separate the reconstruction and classification process which leave rooms for further optimization or form a complicated training model which cannot be resolved efficiently. In this paper, we first propose extracting the spatial pyramid representation as the image feature which forms the foundation of dictionary learning and sparse coding. Then we develop a novel sparse coding model which can learn the dictionary and classifier simultaneously in which form we can get the optimal result and can be solved efficiently by K-SVD. Experiments show that the suggested approach, in terms of classification accuracy and computation time, outperforms other well-known approaches.     

盲环境下稀疏编码监控视频图像降噪仿真

针对盲环境监控视频图像降噪问题,以及当前图像降噪方法中存在的运行效率较低、降噪图像失真度较高等不足之处,结合稀疏编码技术,提出盲环境下稀疏编码监控视频图像降噪方法。根据稀疏表示理论,将其扩展应用到监控视频图像中,利用正交匹配追踪算法对待处理图像进行稀疏编码;采用自适应方式从含噪图像块样本中获取字典,结合自变量分解及拉格朗日算法进行相关问题求解,并据此对图像稀疏编码系数进行优化;结合噪声模型与图像系统的观察模型,对待处理图像进行噪声估计,根据全部噪声估计均值进行图像降噪处理。仿真结果表明,所提盲环境下稀疏编码监控视频图像降噪方法的图像降噪效果优于实验对比方法,且降噪处理时间更短,具有较好的鲁棒性。     

A new steganography algorithm based on video sparse representation

Steganography has been a great interest since long time ago. There are a lot of methods that have been widely used since long past. Recently, there has been a growing interest in the use of sparse representation in signal processing. Sparse representation can efficiently model signals in different applications to facilitate processing. Much of the previous work was focused on image and audio sparse representation for steganography. In this paper, a new steganography scheme based on video sparse representation (VSR) is proposed. To exploit proper dictionary, KSVD algorithm is applied to DCT coefficients of Y component related to video (cover) frames. Both I and Q components of video frames are used for secure message insertion. The aim is to hide secret messages into non-zero coefficients of sparse representation of DCT called, I and Q video frames. Several experiments are performed to evaluate the performance of the proposed algorithm, in case of some metrics such as pick signal to noise ratio (PSNR), the hiding ratio (HR), bit error rate (BER) and similarity (Sim) of secret message, and also runtime. The simulation results show that the proposed method exhibits appropriate invisibility and robustness.

     

应用谱回归和图正则最小二乘回归的数据降维

数据降维对于提高高维数据处理的效率具有重要意义,稀疏编码是目前受到广泛关注的主流降维方法。针对该方法在降维过程中不能保持样本空间几何结构信息的不足,提出一种基于谱回归和图正则最小二乘回归的改进方案,以2个图像数据集和2个基因表达数据集为样本的实验表明该方法优于未加改进的稀疏编码降维法。     

图正则化迁移稀疏概念编码的跨域图像分类

为克服不同图像域之间的特征“差异”，跨越分布“鸿沟”，提出了一种基于正则化迁移稀疏概念编码的跨域图像分类方法。将图像域间的分布差异性和标签相关性信息融入稀疏编码模型中，以学习跨域图像的鲁棒性稀疏表示，从高维的图像特征空间中挖掘图像低维流形结构，形成基向量集，构造跨域图像的迁移稀疏概念编码。该方法挖掘不同图像域之间的共同特征表达，实现了图像标签的跨域迁移。通过在多个图像数据库中的比较实验表明，该方法获得更为鲁棒的图像特征表达，其分类性能显著优于其他相关比较方法。     

基于压缩感知的SAR图像鲁棒编码传输

目的 尽管传统的联合信源信道编码方案可以获得高效的压缩性能,但当信道恶化超过信道编码的纠错能力时会导致解码端重构性能的急剧下降;为此利用压缩感知的民主性提出一种鲁棒的SAR图像编码传输方案,且采用了一系列方法提高该方案的率失真性能。方法 考虑到SAR图像丰富的边缘信息,采用具有更强方向表示能力的方向提升小波变换(DLWT)对SAR图像进行稀疏表示,且为消除压缩感知中恢复非稀疏信号时存在的混叠效应,采用了稀疏滤波方法保证大系数的精确恢复,在解码端采用了高效的Bayesian重建算法获得图像的高性能重建。结果 在同等码率下,与传统的联合信源信道编码方案CCSDS-RS相比,本文方案可以实现更加鲁棒的编码传输,当丢包率达到0.05时,本文方案DSFB-CS获得的重建性能明显要高于CCSDS-RS;与基于Bayesian重建算法TSW-CS的传统方案相比,本文方案可提高峰值信噪比(PSNR)3.9 dB。结论 本文方案DSFB-CS 实现了SAR图像的鲁棒传输,随着丢包率的上升,DSFB-CS获得的重建性能缓慢下降,保证了面对不稳定信道时,解码端可以获得相对稳定的重构图像。     

面向视频语义分析的局部敏感的可鉴别稀疏表示

视频语义分析已经成为人们研究的热点。在传统稀疏表示方法中,相似视频特征未必能产生相近稀疏表示结果。在基于稀疏表示的视频语义分析中,假定相似的视频数据样本的稀疏表示也相似,即两个相似视频特征的稀疏系数之间的距离较小。为了提高视频语义分析的准确性,基于该假设提出一种面向视频语义分析的局部敏感的可鉴别稀疏表示方法。该方法在局部敏感稀疏表示中引入基于稀疏系数的鉴别损失函数,优化构建稀疏表示的字典,使稀疏表示特征满足类内离散度小、类间离散度大的Fisher准则,并建立可鉴别稀疏模型。为验证所提方法的有效性,在相关视频数据库中将其与多种算法进行对比,实验结果表明,该方法显著地提高了视频特征稀疏表示的鉴别性,有效地提高了视频语义分析的准确性。     

基于K-SVD的偏微分方程模型在毫米波图像恢复中的应用

在图像被大噪声污染或具有较低分辨率时,传统的偏微分方程(PDE)模型的稳态解会产生明显的阶梯效应,恢复图像质量较差。针对此缺点,提出了一种新的基于K-奇异值分解(K-SVD)的PDE图像恢复方法,并应用于毫米波(MMW)图像的恢复。K-SVD是一种图像稀疏表示方法,对图像进行稀疏估计的同时实现去噪,对噪声方差较大的图像具有较好的去噪鲁棒性。首先采用K-SVD对MMW图像进行去噪,对去噪图像再应用全变分(TV)模型的PDE方法进行恢复。对所提出的算法分别使用模拟的MMW图像和真实的MMW图像进行测试,并进一步和K-SVD、PDE方法比较,同时使用峰值信噪比(PSNR)对恢复图像进行评价。根据不同噪声方差下的PSNR数据和恢复图像的视觉效果,实验结果证明了所提方法能够有效地恢复MMW图像。     

Ultrasound image restoration based on a learned dictionary and a higher-order MRF

The restoration of images degraded by blur and multiplicative noise is a critical preprocessing step in medical ultrasound images which exhibit clinical diagnostic features of interest. This paper proposes a novel non-smooth non-convex variational model for ultrasound images denoising and deblurring motivated by the successes of sparse representation of images and FoE based approaches. Dictionaries are well adapted to textures and extended to arbitrary image sizes by defining a global image prior, while FoE image prior explicitly characterizes the statistics properties of natural image. Following these ideas, the new model is composed of the data-fidelity term, the sparse and redundant representations via learned dictionaries, and the FoE image prior model. The iPiano algorithm can efficiently deal with this optimization problem. The new proposed model is applied to several simulated images and real ultrasound images. The experimental results of denoising and deblurring show that proposed method gives a better visual effect by efficiently removing noise and preserving details well compared with two state-of-the-art methods.     

Denoising for Multiple Image Copies through Joint Sparse Representation

This paper addresses the recovery of original images from multiple copies corrupted with the noises, which can be represented sparsely in some dictionary. Sparse representation has been proven to have strong ability to denoise. However, it performs suboptimally when the noise is sparse in some dictionary. A novel joint sparse representation (JSR)-based image denoising method is proposed. The images can be recovered well from multiple noisy copies. All copies share a common component—the image, while each individual measurement contains an innovation component—the noise. Our method can separate the common and innovation components, and reconstruct the images with the sparse coefficients and the dictionaries. Experiment results show that the performance of the proposed method is better than that of other methods in terms of the metric and the visual quality.     

基于黎曼流形稀疏编码的图像检索算法

针对视觉词袋（Bag-of-visual-words,BOVW）模型直方图量化误差大的缺点,提出基于稀疏编码的图像检索算法.由于大多数图像特征属于非线性流形结构,传统稀疏编码使用向量空间对其度量必然导致不准确的稀疏表示.考虑到图像特征空间的流形结构,选择对称正定矩阵作为特征描述子,构建黎曼流形空间.利用核技术将黎曼流形结构映射到再生核希尔伯特空间,非线性流形转换为线性稀疏编码,获得图像更准确的稀疏表示.实验在Corel1000和Caltech101两个数据集上进行,与已有的图像检索算法对比,提出的图像检索算法不仅提高了检索准确率,而且获得了更好的检索性能.     

Multi-Scale Deep Cascade Bi-Forest for Electrocardiogram Biometric Recognition

Electrocardiogram (ECG) biometric recognition has emerged as a hot research topic in the past decade.Although some promising results have been reported,especially using sparse representation learning (SRL) and deep neural network,robust identification for small-scale data is still a challenge.To address this issue,we integrate SRL into a deep cascade model,and propose a multi-scale deep cascade bi-forest (MDCBF) model for ECG biometric recognition.We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise.Then we propose a deep cascade framework,which includes multi-scale signal coding and deep cascade coding.In the former,we design an adaptive weighted pooling operation,which can fully explore the discriminative information of segments with low noise.In deep cascade coding,we propose level-wise class coding without backpropagation to mine more discriminative features.Extensive experiments are conducted on four small-scale ECG databases,and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.     

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.     

小波域中双稀疏的单幅图像超分辨

目的 过去几年,基于稀疏表示的单幅图像超分辨获得了广泛的研究,提出了一种小波域中双稀疏的图像超分辨方法。方法 由小波域中高频图像的稀疏性及高频图像块在空间冗余字典下表示系数的稀疏性,建立了双稀疏的超分辨模型,恢复出高分辨率图像的细节系数;然后利用小波的多尺度性及低分辨率图像可作为高分辨率图像低频系数的逼近的假设,超分辨图像由低分辨率图像的小波分解和估计的高分辨率图像的高频系数经过二层逆小波变换来重构。结果 通过大量的实验发现,双稀疏的方法不仅较好地恢复了图像的局部纹理与边缘,且在噪声图像的超分辨上也获得了不错的效果。结论 与现在流行的使用稀疏表示的超分辨方法相比,双稀疏的方法对噪声图像的超分辨效果更好,且计算复杂度减小。