联合非均匀采样和压缩感知的图像压缩算法

为了提高图像重构精度,改善纹理区域视觉效果,本文将压缩感知理论与图像压缩相结合,并提出了一种新的采样方法:在编码端对图像高频部分边缘点进行密集采样,对非边缘部分进行随机抽样,取代了传统压缩感知理论中直接使用测量矩阵获得低维观测值的过程。在解码端利用采样点位置信息构造块测量矩阵,使用光滑l0范数（Smoothed l0,SL0）重构算法实现重叠块重构,最终将其与图像低频部分下采样点插值放大结果合并实现高精度重构。实验结果表明:本文算法不仅可以提高整幅图像和纹理区域的重构精度,而且在低采样率或图像尺寸较小的情况下,算法效率也有明显提升。      

基于压缩感知的井下图像压缩

为了解决石油测井电视图像的实时有效传输和测井系统数据传输率低下的矛盾,提出了一种基于压缩感知的分块图像压缩方法,在满足测井系统的特殊要求,例如器件简单、功耗低、数据储存量小等,同时有效的压缩和重构图像.通过对井下图像的实验,分析压缩然后重构的图像,结果表明采用该方法能在简单的线性测量下得到高压缩比,并且重构后的图像仍能满足测井需要,符合测井系统的要求,提高了井下数据传输率.     

基于非局部相似性和交替迭代优化算法的图像压缩感知

压缩感知理论突破了信号带宽对奈奎斯特采样定理的限制,并且实现了在数据采样的同时进行压缩。目前压缩感知系统通常利用图像在某个变换域具有稀疏性的先验知识,从少量观测值中重构原始图像。本文利用图像像素的邻域结构信息及图像子块的相似性,将图像的非局部相似性作为先验知识运用到压缩感知图像重构中。结合图像的非局部相似性及其在变换域的稀疏性先验知识,提出了基于非局部相似性和交替迭代优化算法的图像压缩感知重构算法,该算法利用迭代阈值法和非局部全变差来交替迭代求解变换域的稀疏性优化问题和非局部相似性的优化问题。实验结果表明,本文算法可以有效提高图像重构的视觉效果和峰值信噪比。      

基于动态方向场的指纹图像增强算法

文章研究了目前的指纹图像增强算法，提出了一种新的指纹方向场的动态计算方法。先利用Radon变换来计算方向场，然后用一组方向滤波器对该图像进行滤波，以达到指纹图像增强的目的。实验结果表明，该算法简单有效，为下一步指纹图像匹配奠定了坚实的基础。     

基于SPL迭代思想的图像压缩感知重构神经网络

由于神经网络强大的学习能力与快速的运行速度,近年来基于深度学习的图像压缩感知(Image Com-pressive Sensing,ICS)研究备受关注.然而,大多数现有ICS神经网络的结构设计忽略了传统迭代重构算法中的数学理论基础,无法有效利用信号中的先验结构知识,可解释性较差.为了保留优化算法核心思想并同时利用深度学习的高性能,本文使用可学习的卷积层替代了传统平滑投影Landweber算法(Smooth Projected Landweber,SPL)中的人工设计参数,提出一种新型ICS神经网络SPLNet.在SPLNet中,设计了一个独特的网络结构SPLBlock实现SPL迭代过程中的三个核心步骤:(1)去除块效应的维纳滤波器;(2)在凸投影集合上的近似操作;(3)实现稀疏表示及去噪的变换域双变量收缩.仿真实验结果表明:与现有最优的ICS优化迭代算法GSR相比,SPLNet的重构图像平均PSNR提升了0.78dB;与最优的神经网络框架SCSNet相比,SPLNet的重构图像平均PSNR提升了0.92dB.     

基于连续方向图的指纹智能预处理算法

文中研究了一种利用Sobel算子提取脊线任意方向的方法,并在融入了指纹图像的特性知识后,提出了一任意方向滤波的指纹智能预处理算法,对于不同质量的指纹图像进行方向滤波,取得了较为理想的结果,实验证明该方法可以较为准确地提取出指纹脊线的方向,对噪化的指纹图像具有较好的鲁棒性。     

基于非局部低秩和加权全变分的图像压缩感知重构算法

为准确有效地实现自然图像的压缩感知(CS)重构,该文提出一种基于图像非局部低秩(NLR)和加权全变分(WTV)的CS重构算法。该算法考虑图像的非局部自相似性(NSS)和局部光滑特性,对传统的全变分(TV)模型进行改进,只对图像的高频分量设置权重,并用一种差分曲率的边缘检测算子来构造权重系数。此外,算法以改进的TV模型与NLR模型为约束构建优化模型,并分别采用光滑非凸函数和软阈值函数来求解低秩和全变分优化问题,很好地利用了图像的自身性质,保护了图像的细节信息,并提高了算法的抗噪性和适应性。仿真结果表明,与基于NLR的CS算法相比,相同采样率下,该文所提算法的峰值信噪比最高可提高2.49 dB,且抗噪性更强,验证了算法的有效性。     

基于稀疏优化的图像压缩感知重建算法

在信号的稀疏表示方法中,传统的基于变换基的稀疏逼近不能自适应性地提取图像的纹理特征,而基于过完备字典的稀疏逼近算法复杂度过高.针对该问题,文章提出了一种基于小波变换稀疏字典优化的图像稀疏表示方法.该算法在图像小波变换的基础上构建图像过完备字典,利用同一场景图像的小波变换在纹理上具有内部和外部相似的属性,对过完备字典进行灰色关联度的分类,有效提高了图像表示的稀疏性.将该新算法应用于图像信号进行稀疏表示,以及基于压缩感知理论的图像采样和重建实验,结果表明新算法总体上提升了重建图像的峰值信噪比与结构相似度,并能有效缩短图像重建时间.     

基于压缩感知的图像压缩抗干扰重构算法

针对传统图像变换压缩方法压缩的图像经无线信道传输时受高斯随机干扰导致重要变换系数失真出现重构图像局部内容缺失的现象,本文根据压缩感知(CS)信号分量具有同等重要性的特性,理论分析了去除失真CS信号分量以抵御干扰的可行性,提出一种基于CS的图像压缩抗干扰重构算法。算法首先假定已知受高斯随机干扰的比特所对应的CS信号分量的位置,然后根据这些位置确定新的CS信号和重构矩阵,再进行阈值迭代重构。仿真结果表明,本文算法在低误码率(BER)下得到精确重构的图像,在高BER下得到图像内容无缺失仅全局质量小幅下降的重构图像。因此,基于CS的图像压缩抗干扰重构算法能够较好地克服变换压缩方法以及阈值迭代重构算法抗干扰能力低的不足,从而为图像无线传输抗高斯随机干扰问题提供一种可行的解决方案。     

基于压缩感知的均匀圆阵方向不变波束优化方法

针对现有的方向不变波束形成算法在阵元个数确定以后,其波束的主瓣与旁瓣水平无法进一步改善的情形下.本文基于均匀圆阵在现有方向不变波束形成方法的基础上,通过引入压缩感知理论(Compressed Sens-ing,CS),提出了一种可以进一步改善波束主瓣与旁瓣水平的方法.该方法使用压缩感知理论对信号进行压缩采样和稀疏重构,...     

Compressive sensing reconstruction via decomposition

When recovering images from a small number of Compressive Sensing (CS) measurements, a problem arises whereby image features (e.g., smoothness, edges, textures) cannot be preserved well in reconstruction, especially textures at small-scale. Since the missing information still remains in the residual measurement, we propose a novel Decomposition-based CS-recovery framework (DCR) which utilizes residual reconstruction and state-of-the-art filters. The proposed method iteratively refines residual measurement which is closely related to the denoise-boosting techniques. DCR is further incorporated with a weighted total variation and nonlocal structures in the gradient domain as priors to form the proposed Decomposition based Texture preserving Reconstruction (DETER). We subsequently demonstrate robustness of the proposed framework to noise and its superiority over the other state-of-the-art methods, especially at low subrates. Its fast implementation based on the split Bregman technique is also presented.     

Imaging via Compressive Sampling [Introduction to compressive sampling and recovery via convex programming]

Image compression algorithms convert high-resolution images into a relatively small bit streams in effect turning a large digital data set into a substantially smaller one. This article introduces compressive sampling and recovery using convex programming.     

A generalization of nonuniform bandpass sampling

Nth-order nonuniform sampling is described for generalized bandpass signal frequency position, bandwidth, sampling rate, frequency-shift and phase-shift. A bandpass extension to the Nyquist criterion is derived, showing that restrictions on bandpass frequency position for odd orders of nonuniform sampling tend to zero as N tends to infinity. Bandpass interpolants based on the sinc function are derived for the generalized Nth-order sampled bandpass signals. It is shown that, for minimum (Nyquist) rate sampling, these interpolants are comprised of N bandpass filters, each with independent phase. The number of bandpass filters comprising the interpolant is found to decrease as the sample rate increases. The advantage of describing Nth-order sampling as the Nth replication and uniform sampling of a signal is demonstrated. Finally, digital implementation of the Nth-order bandpass sampling interpolants is discussed. It is established that it is not practicable to attempt to perform nonuniform bandpass sampling at the theoretical minimum rate, where the interpolation is to be performed digitally     

Optimal sub-Nyquist nonuniform sampling and reconstruction formultiband signals

We study the problem of optimal sub-Nyquist sampling for perfect reconstruction of multiband signals. The signals are assumed to have a known spectral support ℱ that does not tile under translation. Such signals admit perfect reconstruction from periodic nonuniform sampling at rates approaching Landau's (1967) lower bound equal to the measure of ℱ. For signals with sparse ℱ, this rate can be much smaller than the Nyquist rate. Unfortunately the reduced sampling rates afforded by this scheme can be accompanied by increased error sensitivity. In a previous study, we derived bounds on the error due to mismodeling and sample additive noise. Adopting these bounds as performance measures, we consider the problems of optimizing the reconstruction sections of the system, choosing the optimal base sampling rate, and designing the nonuniform sampling pattern. We find that optimizing these parameters can improve system performance significantly. Furthermore, uniform sampling is optimal for signals with ℱ that tiles under translation. For signals with nontiling ℱ, which are not amenable to efficient uniform sampling, the results reveal increased error sensitivities with sub-Nyquist sampling. However, these can be controlled by optimal design, demonstrating the potential for practical multifold reductions in sampling rate     

A stable nonuniform sampling expansion involving derivatives

The work of D.A. Linden and N.M. Abramson (Inf. Control, vol.3, p.26-31, 1960, and vol.4, p.95-6, 1961) is completed in the sense that a full investigation of the convergence properties of the expansion is made. By adapting the work of N. Levinson (Am. Math. Soc. Colloq. Pub. vol.26, 1940) on nonharmonic Fourier series and general results from the theory of B-splines, the author extends the result to the case of sampling with uniformly spaced samples. A stability property is proved     

Coupling orientation diffusion with coherence-enhancing diffusion: a fingerprint case

The enhancement of coherent flow-like structures is desired for many image processing tasks, such as segmentation and feature detection. This task can be accomplished in a natural way by adopting anisotropic diffusion filtering using a diffusion matrix adapted to the local structure. This method is referred to as coherence-enhancing diffusion (CED). The performance of CED can be analyzed by observing the evolution of the orientation field (OF) associated with an evolving diffusion matrix. It was revealed from a series of experiments that the final OF from a CED-enhanced image sometimes strays from its true underlying OF (marked by a human expert), degrading its performance. In this paper, a strategy is proposed which repeatedly cleans the OF associated with a diffusion matrix. Thus, for every iteration of CED, its OF is diffused separately until it converges and is then fed back to the CED process to move forward. This hypothesis is tested with the motive of getting an enhanced CED performance. The proposed scheme is validated using fingerprint data, and their numerical results are displayed.     

有向扩散的无线传感器分簇路由协议

在无线传感器网络中,一种合理的路由算法对提高网络寿命有着非常重要的作用,基于传统的分簇路由协议LEACH,以延长网络寿命和提高网络通信质量提出了一种新的路由算法.这种路由算法引入了有向天线的使用.算法在定向传播的基础上不但节省了网络能量,并且灵活的簇群配置,簇头交换策略使得这个新的算法不局限于特定拓扑分布无线传感器网络.算法的创新点在于利用天线的方向形成簇群,同时达到了在数据传输时能量节省和网络路由变化通信开销减少的目的.     

利用组稀疏特性的宽带压缩频谱感知

基于认知无线电的动态频谱接入需对宽带信道进行频谱感知,而越来越高的采样速率日益成为宽带频谱感知的瓶颈。压缩感知作为一种新的信号获取技术为亚奈奎斯特采样速率下的宽带频谱感知提供了一种可行方案。在相关应用场景中,如果能够挖掘相关先验信息并在重构算法中整合这些信息,将大幅提高压缩感知的性能。本文基于压缩感知技术,利用信道的划分信息及宽带信号的组稀疏特性,提出了一种组稀疏贪婪算法GOMP。该方法在成熟的贪婪算法基础上,利用子信道内多频点的组测量信息,根据组测量的概率分布特性来识别宽带信道的活动子信道。这种组测量识别方式使算法能以较少的观测数据实现对宽带信道的快速准确感知,极大地降低了宽带频谱感知所需的采样速率。实验结果表明:该算法比传统的OMP算法及BP算法不仅具有更好的重构效果及频谱检测性能,而且具有更好的压缩性能及实时性能。      

Compressive sensing based high-resolution passive bistatic radar

Passive bistatic radar (PBR) systems using different communication signals can only offer low-resolution target detection due to their inherent low bandwidth. In this paper, compressive sensing (CS) is applied to multichannel FM and GSM PBR to achieve improved range-Doppler resolutions and avoid some limitations of classical multiband PBR processing. In CS context, block-structured time-domain dictionary which is formed from multichannel signals suffers from coherence when fine range resolution is employed. To overcome such a pitfall, this work first transforms the dictionary to spectral domain where only the most important spectral components are retained. Principle component analysis followed by a whitening method are then applied to this spectrally transformed data in order to reduce the dimensionality of problem, thereby reducing the dictionary size and most importantly fulfilling the required condition of dictionary incoherence for better CS-based recovery. Two different block-structured dictionary formations are tested. The performance of the recovery of spatially close targets, in both FM and GSM PBR setups, are reported.