Nonlocal regularization based on DT-CWT for WMSN video image iterative reconstruction

The prior knowledge of signal is the previous condition of image compressed sensing reconstruction. In order to improve the quality of the priors except for image sparsity, this paper proposes a new model of video image reconstruction. The texture is the important visual feature of video image as a result of its repeat, leading to image global geometrical structures. The nonlocal idea comes from image self-familiar and can represent image detail features from the geometrical point of view. Therefore, the texture geometrical feature of video image is researched, and we take advantage of dual-tree complex wavelet transform to portray the sparsity representation regularization of the texture. What is more, global constrained regularization is constructed with the help of the nonlocal idea. On the basis of the two regularizations above, a new reconstruction model of video image compressed sensing is proposed, which not only preserves the sparsity prior knowledge of image but also improves the quality of prior knowledge of image by promoting geometrical structure. Iterative shrinkage thresholding algorithm is adopted to solve the model leading to a both simple and quick iterative algorithm. Numerical experiments show that our method is efficient for video image recovery, especially preserving the global details of the original video image.     

双树小波变换与小波树稀疏联合的低场CS-MRI算法

压缩感知理论常用在磁共振快速成像上,仅采样少量的K空间数据即可重建出高质量的磁共振图像.压缩感知磁共振成像技术的原理是将磁共振图像重建问题建模成一个包含数据保真项、稀疏先验项和全变分项的线性组合最小化问题,显著减少磁共振扫描时间.稀疏表示是压缩感知理论的一个关键假设,重建结果很大程度上依赖于稀疏变换.本文将双树复小波变换和小波树稀疏联合作为压缩感知磁共振成像中的稀疏变换,提出了基于双树小波变换和小波树稀疏的压缩感知低场磁共振图像重建算法.实验表明,本文所提算法可以在某些磁共振图像客观评价指标中表现出一定的优势.     

基于第二代小波的超谱遥感图像融合算法研究

超谱遥感图像包含了大量的波段．波段之间的相关性较高．采用信息融合技术可以降低超谱图像的分析难度。提出了一种结构新颖的第二代小波加权融合算法。首先将图像分解为两个序列．用2阶Neville滤波器构造预测和更新算子．对两个序列以矩形栅格和梅花形栅格的格式进行交替预测和更新;再以各个波段的方差作为融合的特征．进行特征级第二代小波加权融合．最后对图像进行第二代小波重构。为了验证新方法的有效性．采用机载可见光-红外成像光谱仪超谱遥感图像进行仿真．并与典型融合方法主成分分析和离散小波变换的融合效果相比较。实验结果表明提出的第二代小波加权融合算法能够很好地保持图像的空间特性和光谱特性．其熵值高于主成分分析融合结果0．1949,高于离散小波变换融合结果0．7998。     

一种基于选择性测量的自适应压缩感知方法

针对低信噪比条件下现有压缩感知系统重构性能严重恶化的问题,提出了一种基于选择性测量的自适应压缩感知结构.首先推导并分析了经过压缩测量的噪声的统计特性及其对重构性能的影响;然后基于输出能量最小化准则,设计了一种压缩域投影滤波联合噪声检测的自适应感知器,感知获得噪声子空间的位置信息;进一步利用该信息构造选择性压缩测量矩阵,智能选择测量信号,同时"屏蔽"噪声分量,极大提高了压缩测量值的信噪比.仿真结果表明,相对于现有压缩感知结构,选择性测量的压缩感知结构明显改善了含噪稀疏信号的重构性能,可更好地应用于吸波材料的前端特性分析、认知无线电的频谱感知等领域.     

基于双边信息的分布式视频压缩感知模型研究

针对传统视频编码技术计算量大和复杂度高的缺点,提出一种基于双边信息的分布式视频压缩感知算法。该算法将压缩感知技术与分布式视频编码技术相结合,把视频序列分为Key帧和CS帧,Key帧运用传统的帧内编码和解码,CS帧编码端运用压缩感知编码,解码端运用视频块内与视频块间的双边信息和梯度投影算法进行优化重构。通过双边信息的运动估计和压缩编码器的设计,实现基于双边信息的分布式视频压缩感知模型的构建。仿真结果表明该模型既可以实现高效编码,又可以实现复杂度由编码端向解码端转移,在较低的采样率下,提高视频的压缩能力和传输速度。     

基于高斯混合尺度模型的压缩传感图像重构

在图像处理领域中,压缩传感重构是稀疏表示下的最重要的病态反问题之一。压缩传感图像重构利用图像可稀疏表示的先验知识,从比奈奎斯特采样率低得多的随机投影观测值中重构原始图像。为了克服传统的压缩传感算法中收敛速度慢和未利用变换系数的邻域统计特性的缺点,提出了基于高斯混合尺度模型的压缩传感图像重构算法,证明了独立的高斯混合尺度分布作为压缩传感重构的稀疏先验知识的可行性,结合全变差调整进一步提高算法的性能。实验结果表明,该算法有效地提高了重构图像的主观视觉效果和峰值信噪比,加快了压缩传感图像重构算法的收敛速度。     

Undersampled MRI reconstruction with patch-based directional wavelets

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a preconstructed basis or dictionary. In this paper, patch-based directional wavelets are proposed to reconstruct images from undersampled k-space data. A parameter of patch-based directional wavelets, indicating the geometric direction of each patch, is trained from the reconstructed image using conventional compressed sensing MRI methods and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. A reconstruction formulation is proposed and solved via an efficient alternating direction algorithm. Simulation results on phantom and in vivo data indicate that the proposed method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise. Besides, the proposed method is not sensitive to the initial image when training directions.     

基于改进的分块压缩感知红外图像重建

针对基于压缩感知理论的红外图像重建问题,提出一种基于改进的分块压缩感知红外图像重建方法。该方法首先对原始红外图像进行分块,并对每个子块用相同的观测矩阵进行随机观测,获得少量的观测数据;然后利用谱图小波变换优异的稀疏特性,将其引入平滑投影Landweber算法进行迭代优化重建,同时采用混合中值滤波进行处理以增加图像的平滑度和减少块伪影,最后输出满足要求的高质量红外图像。实验结果表明,在相同采样率下,该方法对于不同类型红外图像的重建性能均优于目前广为采用的一些小波压缩感知方法,可获得更高质量的红外图像。     

Multi-Image Compression–Encryption Algorithm Based on Compressed Sensing and Optical Encryption

In order to achieve large-capacity, fast and secure image transmission, a multi-image compression–encryption algorithm based on two-dimensional compressed sensing (2D CS) and optical encryption is proposed in this paper. Firstly, the paper uses compressed sensing to compress and encrypt multiple images simultaneously, and design a new structured measurement matrix. Subsequently, double random phase encoding based on the multi-parameter fractional quaternion Fourier transform is used to encrypt the multiple images for secondary encryption, which improves the security performance of the images. Moreover, a fractional-order chaotic system with more complex chaotic behavior is constructed for image compression and encryption. Experimental results show that the algorithm has strong robustness and security.     

基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知

针对在实际宽带压缩频谱感知中难以预先获知宽带频谱稀疏度的问题,提出一种改进的稀疏度自适应匹配追踪(modified sparsity adaptive matching pursuit, MSAMP)算法,该算法在支撑集选择过程中对稀疏度进行了预估计。结合序贯压缩检测技术,给出了一种基于该算法的多认知用户合作场景下的宽带压缩频谱感知方法,理论分析和实验仿真结果表明,该方法可在频谱稀疏度先验知识缺少的情况下,有效提高宽带频谱感知性能。     

Optical image encryption using chaos-based compressed sensing and phase-shifting interference in fractional wavelet domain

In this paper, a novel optical image encryption system combining compressed sensing with phase-shifting interference in fractional wavelet domain is proposed. To improve the encryption efficiency, the volume data of original image are decreased by compressed sensing. Then the compacted image is encoded through double random phase encoding in asymmetric fractional wavelet domain. In the encryption system, three pseudo-random sequences, generated by three-dimensional chaos map, are used as the measurement matrix of compressed sensing and two random-phase masks in the asymmetric fractional wavelet transform. It not only simplifies the keys to storage and transmission, but also enhances our cryptosystem nonlinearity to resist some common attacks. Further, holograms make our cryptosystem be immune to noises and occlusion attacks, which are obtained by two-step-only quadrature phase-shifting interference. And the compression and encryption can be achieved in the final result simultaneously. Numerical experiments have verified the security and validity of the proposed algorithm.     

A novel image fusion method using WBCT and PCA

A novel image fusion algorithm based on wavelet-based contourlet transform （WBCT） and principal component analysis （PCA） is proposed. The PCA method is adopted for the low-frequency components. Using the proposed algorithm to choose the greater of the active measures, the region consistency test is performed for the high-frequency components. Experiments show that the proposed method works better in preserving the edge and texture information than wavelet transform method and Laplacian pyramid （LP） method do in image fusion. Four indicators for the fusion image are given to compare the proposed method with other methods.     

字典学习稀疏表示的高光谱图像异常检测

针对稀疏表示高光谱检测算法性能受背景字典影响较大的问题,充分利用高光谱图像空间信息和光谱主成分信息,提出了一种基于字典学习的稀疏表示异常检测算法。首先利用主成分分析提取高光谱数据的主特征,建立目标主成分空间,并证明了在主成分空间进行字典学习稀疏重构的可行性;然后在主成分空间内构造基于K-SVD算法的训练字典,改善了背景字典性能;采用正交匹配算法重构主成分分量,利用主成分分析反变换得到待检测像元重构光谱,增强了高光谱图像的局部异常特性;最后,基于重构误差异常特性实现高光谱图像异常检测。仿真结果证明了该方法的有效性。     

基于光谱技术的航天育种番茄品种鉴别方法的研究

为了实现航天育种番茄不同品种的快速光谱鉴别,采用主成分分析法对光谱数据进行聚类分析,并将小波变换用于对大量光谱数据的压缩,同时结合神经网络建立了番茄品种鉴别模型.该模型将压缩后的数据作为神经网络的输入,加速了神经网络的训练速度.通过对太空育种突变株M1和M2及其亲本番茄品种的共105个番茄叶片样本建立训练模型,并用每个品种15个样本,共45个番茄叶片的样本进行预测.结果表明,用该方法对航天育种番茄不同品种的鉴别正确率达到97.8%.说明文章提出的方法具有很好的分类和鉴别作用,为航天育种番茄不同品种的快速鉴别提供了一种新方法.     

A Pan-sharpening method appropriate to vegetation applications

An improved Pan-sharpening algorithm appropriate to vegetation applications is proposed to fuse a set of IKONOS panchromatic （PAN） and multispectral image （MSI） data. The normalized difference vegetation index （NDVI） is introduced to evaluate the quality of fusion products. Compared with other methods such as principal component analysis （PCA）, wavelet transform （WT）, and curvelet transform （CT）, this algorithm has a better trade-off between keeping the spatial and spectral information. The NDVI performances indicate that the fusion product of this method is more suitable for vegetation applications than the other methods.     

基于亮温光谱和主成分分析的大气污染气体探测

在移动平台条件下,污染气体红外遥测系统无法获取有效的背景光谱。利用算法扣除红外光谱中的背景信息,提取目标光谱特征,是污染气体红外遥测系统在移动平台上应用的关键环节。提出基于亮温光谱和主成分分析的迭代拟合算法。相对于传统的亮温光谱法,该算法降低了亮温光谱法对仪器响应函数的精度要求,同时结合主成分分析和迭代拟合技术,可有效提取目标光谱特征。     

基于压缩感知与扩展小波树的自适应压缩成像

为了在现有的采样条件下,通过新的压缩采样方式获得计算量小且质量更好的图像,提出了基于压缩感知与扩展小波树的自适应压缩成像方法。首先将图像投影到分区控制的DMD上,获得图像在低分辨率下的测量值,并通过压缩感知重构算法重构出低分辨图像,接着利用扩展小波树预测重要小波位置,通过DMD在小波域采样获取图像的细节信息,最后由小波逆变换恢复高分辨率图像。将该方法与最小化全变分算法（TVAL3）和近来提出的基于扩展小波树的自适应成像算法（EWT-ACS）效果进行对比,实验结果表明,以boat图像为例,在压缩感知采样率为0.75,整体采样率为10%的无噪声条件下,该方法相较于TVAL3、EWT-ACS算法信噪比提高了4.63 dB和2.87 dB,在附加噪声条件下成像效果也较好。该方法能极大地降低压缩感知重建算法的运行时间,同时减少采样次数,具有较好的抗噪性。     

Three dimension double inversion recovery gray matter imaging using compressed sensing

The double inversion recovery (DIR) imaging technique has various applications such as black blood magnetic resonance imaging and gray/white matter imaging. Recent clinical studies show the promise of DIR for high resolution three dimensional (3D) gray matter imaging. One drawback in this case however is the long data acquisition time needed to obtain the fully sampled 3D spatial frequency domain (k-space) data. In this paper, we propose a method to solve this problem using the compressed sensing (CS) algorithm with contourlet transform. The contourlet transform is an effective sparsifying transform especially for images with smooth contours. Therefore, we applied this algorithm to undersampled DIR images and compared with a CS algorithm using wavelet transform by evaluating the reconstruction performance of each algorithm for undersampled k-space data. The results show that the proposed CS algorithm achieves a more accurate reconstruction in terms of the mean structural similarity index and root mean square error than the CS algorithm using wavelet transform.     

一种基于压缩感知的三维导体目标电磁散射问题的快速求解方法

提出了一种将压缩感知和特征基函数结合的方法来计算三维导体目标的雷达散射截面.利用压缩感知理论,将随机选择的矩量法阻抗矩阵作为测量矩阵,将激励电压视为测量值,然后再用恢复算法可实现二维或二维半目标感应电流的求解.对于三维导体目标,使用Rao-Wilton-Glisson基函数表示的感应电流在常用的离散余弦变换基、小波基等稀疏基上不稀疏.为此,本文将计算出的目标特征基函数作为稀疏基,用广义正交匹配追踪算法作为恢复算法来加速恢复过程,并应用到三维导体目标的雷达散射截面计算中.数值结果证明了本文方法的准确性与高效性.     

A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

In this paper,a compressive sensing(CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed.The transform domain coefficients of the original image are scrambled by Arnold map firstly.Then the watermark is adhered to the scrambled data.By compressive sensing,a set of watermarked measurements is obtained as the watermarked cipher image.In this algorithm,watermark embedding and data compression can be performed without knowing the original image;similarly,watermark extraction will not interfere with decryption.Due to the characteristics of CS,this algorithm features compressible cipher image size,flexible watermark capacity,and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss.Simulation results and analyses show that the algorithm achieves good performance in the sense of security,watermark capacity,extraction accuracy,reconstruction,robustness,etc.