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.     

Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.     

Correspondence normalized ghost imaging on compressive sensing

Ghost imaging(GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios(SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing(CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing(CS) and time-correspondence imaging(CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale "double-slit" image, the mean square error(MSE) by GI and the normalized GI(NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021by CCNGI scheme with 2500 measurements. For the eight-grayscale "lena" object, the peak signal-to-noise rates(PSNRs)are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale "double-slit" object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints(GISC) with a shorter reconstruction time.     

基于压缩感知理论的~(252)Cf源中子脉冲信号分析

针对252Cf源驱动核材料产生裂变中子脉冲信号具有脉冲序列特殊的"0,1"稀疏结构之特点,采用压缩感知理论,通过巧妙引入图论中的二分图模型,同时结合二分图的最小覆盖性质,适当添加约束条件,构建了稀疏均匀的观测矩阵。研究结果表明,利用压缩感知理论对"0,1"中子脉冲序列特殊稀疏结构的信号重构算法不仅可行,而且还获得了优于l1范数最小化方法重构结果,这对252Cf驱动核材料的中子脉冲信号分析与处理提供了一种新的途径或方法。     

Block-wise motion detection using compressive imaging system

A block-wise motion detection strategy based on compressive imaging, also referred to as feature-specific imaging (FSI), is described in this work. A mixture of Gaussian distributions is used to model the background in a scene. Motion is detected in individual object blocks using feature measurements. Gabor, Hadamard binary and random binary features are studied. Performance of motion detection methods using pixel-wise measurements is analyzed and serves as a baseline for comparison with motion detection techniques based on compressive imaging. ROC (Receiver Operation Characteristic) curves and AUC (Area Under Curve) metrics are used to quantify the algorithm performance. Because a FSI system yields a larger measurement SNR(Signal-to-Noise Ratio) than a traditional system, motion detection methods based on the FSI system have better performance. We show that motion detection algorithms using Hadamard and random binary features in a FSI system yields AUC values of 0.978 and 0.969 respectively. The pixel-based methods are only able to achieve a lower AUC value of 0.627.     

基于压缩感知的差分关联成像方案研究

关联成像可提供一种运用常规手段难以获得清晰图像的方法, 能够解决一些常规成像技术不易解决的问题, 是近些年来量子光学领域的前沿和热点之一.本文提出一种基于压缩感知的差分关联成像方案(简称, 差分压缩关联成像方案), 将高斯分布的热光源强度分布作为压缩感知的测量矩阵, 差分物体信息作为被成像物体信息, 利用差分关联成像方案的高成像信噪比和压缩感知技术的低采样次数, 通过正交匹配追踪算法, 高质量地恢复出物体信息. 并以二灰度“双缝”、“NUPT”, 多灰度Lena图和Boats图为例, 数值仿真差分压缩关联成像过程; 同时将本方案350次测量的结果与差分关联成像方案30000次测量的结果进行对比, 研究结果表明针对不同的被成像物体(二灰度“双缝”、“NUPT”, 以及多灰度Lena图和Boats图), 10次成像的均方误差平均值分别降低了97.7%, 93.9%, 92.5%和71.4%; 与压缩鬼成像方案相比, 同样测量次数条件下均方误差值对于二灰度双缝和多灰度Lena图、Boats图等目标物 体分别有50.4%, 72.9%和66.8%的降低. 差分压缩关联成像方案极大地提高了成像信噪比, 降低了成像时间. 关键词： 关联成像 差分 压缩感知 均方误差     

复杂场景下基于压缩感知的目标电磁散射与成像

研究了处于复杂场景下目标的逆合成孔径雷达(ISAR)成像问题。首先,建立了目标与复杂环境的电磁散射模型,采用计算电磁学的方法仿真得到了目标的雷达回波数据,进而充分考虑了背景噪声对雷达成像质量的影响。研究发现,目标所处的复杂背景会降低ISAR对目标的成像质量。其次,为减小仿真雷达回波数据所需的计算量,提出采用基于压缩感知(CS)的方法来对该场景进行成像,从而极大降低电磁仿真的计算点数。通过实验发现,在CS成像中,采用数据点使用率为0.4时所得到的成像质量可达到采用转台成像质量的效果。因此,采用基于CS的成像方法,可极大降低目标与场景的电磁散射计算复杂度,使得处于真实复杂场景下的目标电磁仿真和ISAR成像研究切实可行。     

基于压缩感知的矢量阵聚焦定位方法

本文针对噪声源近场定位识别问题,利用声源分布在空间域具有稀疏性,在压缩感知理论框架下建立了新体系下的矢量阵聚焦波束形成方法,用于解决同频相干声源的定位识别问题.新方法可在小快拍下准确获得噪声源的空间位置,且不损失对噪声源贡献相对大小的评价能力.通过详细的理论推导、仿真分析和试验验证,证明了基于压缩感知的矢量阵聚焦定位新方法本质上实现了l1范数正则化求解下的波形恢复和空间谱估计,因此具有较高的定位精度,较强的相干声源分辨能力、准确的声源贡献相对大小评价能力以及较高的背景压制能力,可应用于水下复杂噪声源的定位识别.     

An infrared image super-resolution reconstruction method based on compressive sensing

Limited by the properties of infrared detector and camera lens, infrared images are often detail missing and indistinct in vision. The spatial resolution needs to be improved to satisfy the requirements of practical application. Based on compressive sensing (CS) theory, this thesis presents a single image super-resolution reconstruction (SRR) method. With synthetically adopting image degradation model, difference operation-based sparse transformation method and orthogonal matching pursuit (OMP) algorithm, the image SRR problem is transformed into a sparse signal reconstruction issue in CS theory. In our work, the sparse transformation matrix is obtained through difference operation to image, and, the measurement matrix is achieved analytically from the imaging principle of infrared camera. Therefore, the time consumption can be decreased compared with the redundant dictionary obtained by sample training such as K-SVD. The experimental results show that our method can achieve favorable performance and good stability with low algorithm complexity.     

Compressed Sensing MRI Using Sparsity Averaging and FISTA

Magnetic resonance imaging (MRI) is widely adopted for clinical diagnosis due to its non-invasively detection. However, acquisition of full k-space data limits its imaging speed. Compressed sensing (CS) provides a new technique to significantly reduce the measurements with high-quality MR image reconstruction. The sparsity of the MR images is one of the crucial bases of CS-MRI. In this paper, we present to use sparsity averaging prior for CS-MRI reconstruction in the basis of that MR images have average sparsity over multiple wavelet frames. The problem is solved using a Fast Iterative Shrinkage Thresholding Algorithm (FISTA), each iteration of which includes a shrinkage step. The performance of the proposed method is evaluated for several types of MR images. The experiment results illustrate that our approach exhibits a better performance than those methods that using redundant frame or a single orthonormal basis to promote sparsity.     

Implementing compressive fractional Fourier transformation with iterative kernel steering regression in double random phase encoding

This paper proposes a novel approach in double random phase encryption based on compressive fractional Fourier transform along with the kernel steering regression. The method increases the complexity of the image by using fractional Fourier transform and taking fewer measurements from the image data. Numerical results are given to analyze the validity of this technique. Considering natural images to be sparse in some domain, we apply a compressive sensing (CS) approach by using a TwIST algorithm. The encryption process has kernel steering regression algorithm for denoising and compressive sensing technique for image compression along with the fractional Fourier transform that makes the image in more complex form.     

Computational ghost imaging with deep compressed sensing

Computational ghost imaging (CGI) provides an elegant framework for indirect imaging, but its application has been restricted by low imaging performance. Herein, we propose a novel approach that significantly improves the imaging performance of CGI. In this scheme, we optimize the conventional CGI data processing algorithm by using a novel compressed sensing (CS) algorithm based on a deep convolution generative adversarial network (DCGAN). CS is used to process the data output by a conventional CGI device. The processed data are trained by a DCGAN to reconstruct the image. Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning. Moreover, the background noise can be eliminated well by this method.     

Single image non-uniformity correction using compressive sensing

A non-uniformity correction (NUC) method for an infrared focal plane array imaging system was proposed. The algorithm, based on compressive sensing (CS) of single image, overcame the disadvantages of “ghost artifacts” and bulk calculating costs in traditional NUC algorithms. A point-sampling matrix was designed to validate the measurements of CS on the time domain. The measurements were corrected using the midway infrared equalization algorithm, and the missing pixels were solved with the regularized orthogonal matching pursuit algorithm. Experimental results showed that the proposed method can reconstruct the entire image with only 25% pixels. A small difference was found between the correction results using 100% pixels and the reconstruction results using 40% pixels. Evaluation of the proposed method on the basis of the root-mean-square error, peak signal-to-noise ratio, and roughness index (ρ) proved the method to be robust and highly applicable.     

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.     

Structured sublinear compressive sensing via belief propagation

Compressive sensing (CS) is a sampling technique designed for reducing the complexity of sparse data acquisition. One of the major obstacles for practical deployment of CS techniques is the signal reconstruction time and the high storage cost of random sensing matrices. We propose a new structured compressive sensing scheme, based on codes of graphs, that allows for a joint design of structured sensing matrices and logarithmic-complexity reconstruction algorithms. The compressive sensing matrices can be shown to offer asymptotically optimal performance when used in combination with orthogonal matching pursuit (OMP) methods. For reduced-complexity greedy reconstruction schemes, we propose a new family of list-decoding belief propagation algorithms, as well as reinforced and multiple-basis belief propagation (BP) algorithms. Our simulation results indicate that reinforced BP CS schemes offer very good complexity–performance tradeoffs for very sparse signal vectors.     

Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain

Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.     

次级声源优化布放的局部空间有源降噪

针对区域有源降噪问题,为获得更优降噪效果,根据实际次级通路传递函数,提出次级声源优化布放的有源控制系统并详细比较了两种次级声源优化布放算法与次级声源均匀布放的实际降噪效果。应用的第一种次级声源优化算法是l2范数约束的约束匹配追踪算法,第二种次级声源优化算法是l1范数约束的稀疏正则化方法。在全消声室中利用扬声器线阵进行多通道有源降噪实验研究,实验结果表明,在200~1000 Hz,次级声源优化布放的控制系统的平均降噪量比次级声源均匀布放的控制系统的平均降噪量多5 dB左右;在1100~1900 Hz,次级声源优化布放的控制系统的平均降噪量比次级声源均匀布放的控制系统的平均降噪量多11~13 dB左右,次级声源优化布放的控制系统的降噪量分布更加均匀且次级声源输出能量更小。此外,两种优化算法中,稀疏正则化方法的降噪效果更佳。     

基于压缩感知超分辨鬼成像

分辨率是成像系统的一个重要参数, 获得高分辨率图像一直是鬼成像系统的一个目标. 本文提出了以成像系统点扩散函数作为先验知识, 基于稀疏测量的超分辨压缩感知鬼成像重建模型. 搭建了一套计算鬼成像实验装置, 用于验证该模型对于提高鬼成像系统分辨率的有效性, 并与传统的鬼成像计算模型进行了对比. 实验表明, 利用该模型可突破成像系统衍射极限分辨率的限制, 得到超分辨鬼成像. 关键词： 鬼成像 压缩感知 超分辨 稀疏测量     

一种结合并行成像和压缩感知的快速磁共振成像新方法

压缩感知（CS）技术和并行成像技术（主要是SENSE技术、GRAPPA技术等）都能通过减少k空间数据的采集量来加快磁共振成像速度,目前已有一些将两种方法相结合进一步加速磁共振成像速度的方法（例如CS-GRAPPA）.本文针对数据采集和重建这两方面对现有CS-GRAPPA方法进行了改进,采集方式上采用了局部等间隔采集模板以满足GRAPPA重建的要求,并对采集模板进行随机放置以满足CS重建的要求;数据重建时,根据自动校正数据估算GRAPPA算法中欠采行的重建误差,并利用误差的大小确定在CS算法中保真的程度.不同磁共振图像重建实验的结果表明：与现有方法相比,本文方法能够更好地保留原有图像细节并有效减少伪影.     

Computational Spectral Imaging Based on Compressive Sensing

Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing(CS). The spectral and spatial information is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens.The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.