Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information

The setting of the measurement number for each block is very important for a block-based compressed sensing system. However, in practical applications, we only have the initial measurement results of the original signal on the sampling side instead of the original signal itself, therefore, we cannot directly allocate the appropriate measurement number for each block without the sparsity of the original signal. To solve this problem, we propose an adaptive block-based compressed video sensing scheme based on saliency detection and side information. According to the Johnson–Lindenstrauss lemma, we can use the initial measurement results to perform saliency detection and then obtain the saliency value for each block. Meanwhile, a side information frame which is an estimate of the current frame is generated on the reconstruction side by the proposed probability fusion model, and the significant coefficient proportion of each block is estimated through the side information frame. Both the saliency value and significant coefficient proportion can reflect the sparsity of the block. Finally, these two estimates of block sparsity are fused, so that we can simultaneously use intra-frame and inter-frame correlation for block sparsity estimation. Then the measurement number of each block can be allocated according to the fusion sparsity. Besides, we propose a global recovery model based on weighting, which can reduce the block effect of reconstructed frames. The experimental results show that, compared with existing schemes, the proposed scheme can achieve a significant improvement in peak signal-to-noise ratio (PSNR) at the same sampling rate.     

混合稀疏水声信道的动态区分性压缩感知估计

由于水声传播过程中同时存在声信号直达、静态或动态边界反射的现象,水声信道会呈现不同动态特性的多径,形成具有混合稀疏的结构,即多径由静态或相对缓变的平稳多径分量和快速时变的动态多径分量混合组成。对于混合稀疏信道,经典的稀疏信道估计算法未考虑混合稀疏性,将导致算法失配、性能下降;以时变稀疏集为模型,动态压缩感知(DCS)结合卡尔曼滤波(KF-CS)可提高对时变多径分量的估计精度,但KF对静态稀疏分量的估计无法充分挖掘其稀疏性。通过将混合稀疏水声信道建模为由静态和时变支撑集所组成的稀疏集,提出一种动态区分性压缩感知(DDCS)方法。该算法首先结合同步正交匹配追踪(SOMP)和正交匹配追踪(OMP)将混合稀疏多径进行区分,分解为静态分量和时变分量;然后,分别用KF-CS和同步正交匹配追踪算法估计时变和静态多径的幅度;最后,将静态分量和时变分量的估计结果整合以得到整个水声信道的冲激响应。通过海试实验把所提DDCS算法与经典信道估计算法、压缩感知算法和DCS算法进行了比较,验证了所提算法的有效性。结果表明,对混合稀疏水声信道进行区分性稀疏估计可改善信道估计性能,进而可通过信道估计均衡器提升水声通信质量。      

相控阵超声缺陷检测数据压缩感知

为了解决超声相控阵信号采集、存储和传输中数据量大的问题,研究了压缩感知在相控阵无损检测信号和图像压缩重构中应用的可行性。首先使用5种贪婪算法对相控阵仿真信号进行压缩重构,根据百分比均方误差选取最优算法并考虑了噪声对精度的影响,结果表明压缩感知可以用低于奈奎斯特极限的测量点数准确重构原始图像;其次用人工缺陷回波信号进行实验验证,通过稀疏度计算选择适用相控阵信号的最优稀疏基,并通过5种传感矩阵的优化选择进一步提高了重构精度。实验结果尽管达不到仿真中的理想效果,但是能以少量测量值准确恢复图像,并能保证缺陷的识别,说明压缩感知算法可以有效提高相控阵缺陷检测效率。此外,在保持测量点数相同的情况下,仿真和实验都研究了不同采样率对重构精度的影响,当测量点数超过一定值时,证实了压缩感知实际与采样率无关。      

A General Rate-Distortion Optimization Method for Block Compressed Sensing of Images

Block compressed sensing (BCS) is a promising technology for image sampling and compression for resource-constrained applications, but it needs to balance the sampling rate and quantization bit-depth for a bit-rate constraint. In this paper, we summarize the commonly used CS quantization frameworks into a unified framework, and a new bit-rate model and a model of the optimal bit-depth are proposed for the unified CS framework. The proposed bit-rate model reveals the relationship between the bit-rate, sampling rate, and bit-depth based on the information entropy of generalized Gaussian distribution. The optimal bit-depth model can predict the optimal bit-depth of CS measurements at a given bit-rate. Then, we propose a general algorithm for choosing sampling rate and bit-depth based on the proposed models. Experimental results show that the proposed algorithm achieves near-optimal rate-distortion performance for the uniform quantization framework and predictive quantization framework in BCS.     

An efficient adaptive arithmetic coding image compression technology

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology.     

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。     

A novel tamper detection scheme for BTC-compressed images

A novel image authentication scheme for the compressed images of block truncation coding (BTC) is proposed in this paper. In the proposed scheme, 1-bit authentication data is generated from the quantization levels of each image block. Multiple block permutations are generated by using the random sequences induced by the selected random number seeds. Multiple copies of the authenticaiton data are embedded into the bit maps of BTC-compressed image blocks based on the block permutations. Experimental results show that the proposed scheme achieves good detecting accuracy while keeping good image quatiy of the embedded image.     

Robust face recognition based on sparse representation in 2D Fisherface space

Sparse representation is being proved to be effective for many tasks in the field of face recognition. In this paper, we will propose an efficient face recognition algorithm via sparse representation in 2D Fisherface space. We firstly transformed the 2D image into 2D Fisherface in preprocessing, and classify the testing image via sparse representation in the 2D Fisherface space. Then we extend the proposed method using some supplementary matrices to deal with random pixels corruption. For face image with contiguous occlusion, we partition each image into some blocks, and define a new rule combining sparsity and reconstruction residual to discard the occluded blocks, the final result is aggregated by voting the classification result of the valid individual block. The experimental results have shown that the proposed algorithm achieves a satisfying performance in both accuracy and robustness.     

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.     

分布式压缩感知麦克风阵列多声源方位估计*

麦克风阵列已被广泛应用于音/视频会议等人机交互领域中时,多声源应用场景对声源方位估计性能提出了更高的要求。压缩感知(CS)声源定位算法将声源定位问题转化为信号的稀疏重构问题,相比传统的定位算法如相位变换加权（SRP-PHAT）和时延累加定位（DS）能够获得较高的定位性能,但多声源的存在一定程度上降低了稀疏程度,影响了CS重构性能。考虑到传统的CS定位算法并未利用多个连续语音帧之间声源空间向量的共同稀疏性,提出采用分布式压缩感知（DCS）理论以改善多声源的稀疏恢复估计的性能。仿真和实验结果表明,相比于传统定位算法和CS-OMP算法,DCS-SOMP算法在不同信噪比和不同声源强度的环境中,对多声源的方位估计都具有更好的定位性能和定位稳健性。     

The rate-distortion optimized quantization algorithm in Compressive Sensing

Rate-distortion optimization greatly improves performance of compression coding system. In this paper, the rate-distortion optimized quantization algorithm is proposed for block-based Compressive Sensing. Compressive Sensing is the emerging technology which can encode a signal into a small number of incoherent linear measurements and reconstruct the entire signal from relatively few measurements. In the algorithm the sampling measurements are quantized optimally based on the rate-distortion theory. For the coefficients near dead-zone the quantization level with best rate-distortion performance is chosen. Moreover, in order to acquire the best performance, a fast Lagrange multiplier solving method is proposed to find the optimal slope λ* of the rate-distortion curve at the given bit budget. Experimental results show that the proposed algorithm improves objective and subjective performances substantially. The average gain about 0.7 dB can be achieved with the same rate.     

Calibration-less multi-coil MR image reconstruction

In parallel magnetic resonance imaging (MRI), the problem is to reconstruct an image given the partial K-space scans from all the receiver coils. Depending on its position within the scanner, each coil has a different sensitivity profile. All existing parallel MRI techniques require estimation of certain parameters pertaining to the sensitivity profile, e.g., the sensitivity map needs to be estimated for the SENSE and SMASH and the interpolation weights need to be calibrated for GRAPPA and SPIRiT. The assumption is that the estimated parameters are applicable at the operational stage. This assumption does not always hold, consequently the reconstruction accuracies of existing parallel MRI methods may suffer. We propose a reconstruction method called Calibration-Less Multi-coil (CaLM) MRI. As the name suggests, our method does not require estimation of any parameters related to the sensitivity maps and hence does not require a calibration stage. CaLM MRI is an image domain method that produces a sensitivity encoded image for each coil. These images are finally combined by the sum-of-squares method to yield the final image. It is based on the theory of Compressed Sensing (CS). During reconstruction, the constraint that "all the coil images should appear similar" is introduced within the CS framework. This leads to a CS optimization problem that promotes group-sparsity. The results from our proposed method are comparable (at least for the data used in this work) with the best results that can be obtained from state-of-the-art methods.     

Alterable-capacity fragile watermarking scheme with restoration capability

To take invisibility and restoration quality into account, this paper proposes an alterable-capacity watermarking scheme. For each block of size 8 × 8 pixels, the alterable-length code is generated based on the roughness of it. The alterable-length watermark generated by the alterable-length code is divided into three parts and embedded in other three blocks based on the secret key. The authenticity of each block is determined by comparing the watermark reconstructed by the block content and the corresponding extracted watermark. To improve the quality of recovered images, two copies of the significant-code of each block are embedded in different blocks and the image inpainting method is adopted to recover the tampered blocks whose significant-code embedded in other blocks is destroyed. The alterable-payload watermark preserves adequate information of image blocks especially for texture images with as few bits as possible and takes into account invisibility, security and restoration quality. Experimental results demonstrate that the proposed scheme improves the quality of watermarked and reconstructed images and is resilient to the known forgery attacks.     

The impact of ADC nonlinearity in a mixed-signal compressive sensing system for frequency-domain sparse signals

Compressive sensing (CS) holds new promises for the digitization of wideband frequency-domain sparse signals at sub-Nyquist rate sampling without compromising the reconstruction quality. In this paper, the impact of ADC nonlinearity in a CS receiver for frequency-domain sparse signals is investigated. In a mixed-signal CS system, signals are randomized before sampling. The signal spectrum at each building block in the mixed-signal CS system is analyzed and compared to a conventional Nyquist-rate sampling system. It is shown that the signal randomization in a mixed-signal CS system is able to spread the spurious energy due to ADC nonlinearity along the signal bandwidth, rather than the concentration of harmonic distortion on a few frequencies as it is the case for a conventional ADC. As a result, this paper shows that a significant ADC SFDR (Spurious Free Dynamic Range) improvement is achieved in a CS receiver when processing sparse signals. Simulation results are reported which are in good agreement with the qualitative analysis.     

压缩感知理论在图像处理领域的应用

针对传统的采样方法得到的图像数据量巨大,给图像信息的后续处理造成极大压力的问题,对压缩感知理论(Compressed Sensing,CS)进行了研究。压缩感知理论使采集很少一部分数据并且从这些少量数据中重构出更大量信息的想法变成可能,突破了奈奎-斯特采样定理的限制。综述了CS理论及关键技术问题,并着重介绍了CS理论在成像系统、图像融合、图像目标识别与跟踪等方面的应用与发展状况。文章指出CS理论开拓了信息处理的新思路,随着该理论的进一步完善,会有更广泛的应用领域。     

压缩感知理论在图像处理领域的应用

针对传统的采样方法得到的图像数据量巨大,给图像信息的后续处理造成极大压力的问题,对压缩感知理论（Compressed Sensing,CS）进行了研究。压缩感知理论使采集很少一部分数据并且从这些少量数据中重构出更大量信息的想法变成可能,突破了奈奎-斯特采样定理的限制。综述了CS理论及关键技术问题,并着重介绍了CS理论在成像系统、图像融合、图像目标识别与跟踪等方面的应用与发展状况。文章指出CS理论开拓了信息处理的新思路,随着该理论的进一步完善,会有更广泛的应用领域。     

一种基于恒定统计的红外图像非均匀性校正算法

对红外焦平面阵列成像系统而言,基于场景的非均匀校正技术是处理固定图案噪声的关键技术。现有的非均匀校正算法主要被收敛速度和鬼像问题所限制。提出一种新的基于恒定统计算法的自适应场景非均匀校正技术。利用红外图像序列的时域统计信息结合提出的α修正均值滤波来估计探测器的参数,通过减少样本的渐进方差估计,完成成像系统的非均匀性校正。通过模拟和真实的非均匀性图像对算法的性能进行评价。实验结果表明,在继承恒定统计算法快速收敛的同时,图像峰值信噪比较恒定校正法及常系数α校正算法分别有44.5%和32.9%的提升,图像鬼像问题有明显改善。     

圆柱腔低频声场的球谐函数分解及声场再现

针对圆柱腔低频声场的球谐函数分解,提出了基于移动式球形传声器阵列(简称球阵)测量的空间域直接求解法。利用空间域测量数据,在压缩感知(Compressive Sensing,CS)理论框架下通过一次线性方程组求解,获得整体坐标系下的声场展开系数。首先讨论了感知矩阵的列相关性,比较不同球阵形式及球面采样点数目,不同球谐函数截断阶数以及空间测量位置等的影响。随后在飞机舱室模型内利用文中提出的方法求解声场展开系数,再现腔内水平面的声压分布,并与传统球坐标变换方法做比较。实验结果表明,利用空间域直接求解法,通过球面随机挑选10个采样点的刚性球阵在声腔3个位置进行声场测量,不仅能够有效求解声场展开系数,而且声场再现精度更高,同时计算效率也显著提高。      

应用于多视点视频编码的基于深度特征的图像区域分割与区域视差估计

基于多视点视图深度特征,提出一种通过简单块匹配运算划分多视点视图区域并估计区域视差的算法.首先基于深度对象的概念确定图像中具有不同深度的区域数量以及这些区域对应的区域视差,再根据误差最小化准则初步确定每个图像块所属区域.当区域中图像块数量小于某个阈值时,采用区域合并算法将该区域中的每个图像块合并到与它的视差最为接近的其它图像区域,通过迭代形成最终的有效图像区域划分.实验表明,该算法能够以图像块为基本单元有效地划分各深度层区域,并准确估计对应的区域视差.     

基于VHDL技术实现视频采集处理器的控制

针对目前视频图像采集技术中图像采样控制复杂,应用不灵活的问题,基于现有视频采样芯片SAA7111,提出一种采用VHDL技术来模拟实现Ｉ２Ｃ总线接口的方法,控制视频采集处理器实现视频图像采集.实验证明,Ｉ２Ｃ总线控制SAA7111采样图像数据正确、稳定.该方法具有非常好的可移植性.