Edge-preserving tomographic reconstruction with nonlocal regularization

Tomographic image reconstruction using statistical methods can provide more accurate system modeling, statistical models, and physical constraints than the conventional filtered backprojection (FBP) method. Because of the ill posedness of the reconstruction problem, a roughness penalty is often imposed on the solution to control noise. To avoid smoothing of edges, which are important image attributes, various edge-preserving regularization methods have been proposed. Most of these schemes rely on information from local neighborhoods to determine the presence of edges. In this paper, we propose a cost function that incorporates nonlocal boundary information into the regularization method. We use an alternating minimization algorithm with deterministic annealing to minimize the proposed cost function, jointly estimating region boundaries and object pixel values. We apply variational techniques implemented using level-sets methods to update the boundary estimates; then, using the most recent boundary estimate, we minimize a space-variant quadratic cost function to update the image estimate. For the positron emission tomography transmission reconstruction application, we compare the bias-variance tradeoff of this method with that of a "conventional" penalized-likelihood algorithm with local Huber roughness penalty.     

一种自适应正则化技术的图像复原方法

针对降质图像的复原问题,在正则化技术解决病态性基础上提出了一种有效的自适应图像复原算法。该方法充分考虑了图像的局部特性,引入了自适应加权矩阵,采用迭代的方法改善算法的收敛性,计算中给予复原图像一定的限制。计算机仿真结果表明,该方法可有效克服模糊退化并再现了原始图像的重要信息,复原图像在峰值信噪比和主观视觉效果方面都有明显的提高。     

基于L曲线的自适应时空正则化视频超分辨率重建

为了解决视频超分辨率重建的病态问题,以得到良好的重建效果,提出了一种新颖的视频超分辨率重建算法。在算法中引入了时空联合正则化算子,通过视频帧本身的空间平滑信息和视频相邻帧的帧间相关先验信息的引入,提高了解的质量;同时,为了选择合适的时空正则化系数,提出了基于L曲线的自适应时空正则化系数计算方法,可以自适应地计算合适的正则化系数。通过对模拟图像序列和真实视频序列的实验结果表明,算法能得到较为精确的解,重建出具有良好视觉效果的高分辨率视频。     

基于分治试探的盲自适应匹配追踪重构算法

压缩感知是一种针对稀疏可压缩信号进行压缩采样的信号处理新方法,针对现有稀疏度探测方法中探测次数较多的问题,基于分治思想提出了盲稀疏度自适应匹配追踪（BSAMP）算法,首先分治试探信号稀疏度,使得其估计值快速逼近真实值,然后通过自适应分组并扩充信号支撑域的方法,快速筛选出有效支撑,并通过弱匹配剪枝得到重构信号。可以在信号稀疏度未知的情况下,快速估计出信号的稀疏度并精确重构出原信号。仿真实验表明：在相同条件下,该算法的重构时间比其他同类算法短,且重构概率也大于其他同类算法。     

最大稀疏稳健恒定束宽波束形成器设计

为了降低宽带阵列恒定束宽的实现复杂性,在分析宽带阵列稀疏性的基础上,构造了以阵元和抽头延迟线(TDL, tapped delay line)稀疏性的凸组合为目标函数,满足恒定束宽约束的波束形成器优化模型,降低了所需的阵元和TDL个数。引入重加权机制,通过序列凸优化,使稀疏性递增并收敛到最大值,证明了保证波束形成器稳健性的范数约束与最大TDL稀疏目标函数之间的等价性。仿真结果表明,可用较少的阵元及TDL个数获得相同的恒定束宽性能,具有工程实用价值。     

Blind compressive sensing using block sparsity and nonlocal low-rank priors

Without knowing the sparsity basis, Blind Compressive Sensing (BCS) can achieve similar results with those Compressive Sensing (CS) methods which rely on prior knowledge of the sparsity basis. However, BCS still suffers from two problems. First, compared with block-based sparsity, the global image sparsity ignores the local image features and BCS approaches based on it cannot obtain the competitive results. Second, since BCS only exploits the weaker sparsity prior than CS, the sampling rate required by BCS is still very high in practice. In this paper, we firstly propose a novel blind compressive sensing method based on block sparsity and nonlocal low-rank priors (BCS-BSNLR) to further reduce the sampling rate. In addition, we take alternating direction method of multipliers to solve the resulting optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly reduce the sampling rate without sacrificing the quality of the reconstructed image.     

High-quality non-blind image deconvolution with adaptive regularization

Non-blind image deconvolution is a process that obtains a sharp latent image from a blurred image when a point spread function (PSF) is known. However, ringing and noise amplification are inevitable artifacts in image deconvolution since perfect PSF estimation is impossible. The conventional regularization to reduce these artifacts cannot preserve image details in the deconvolved image when PSF estimation error is large, so strong regularization is needed. We propose a non-blind image deconvolution method which preserves image details, while suppressing ringing and noise artifacts by controlling regularization strength according to local characteristics of the image. In addition, the proposed method is performed fast with fast Fourier transforms so that it can be a practical solution to image deblurring problems. From experimental results, we have verified that the proposed method restored the sharp latent image with significantly reduced artifacts and it was performed fast compared to other non-blind image deconvolution methods.     

An adaptive image sparse reconstruction method combined with nonlocal similarity and cosparsity for mixed Gaussian-Poisson noise removal

Compressed sensing (CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation (NCSR), in terms of both visual results and quantitative measures.     

基于自适应下采样和超分重建的图像压缩框架

针对联合图像专家组(JPEG)标准设计了一种基于自适应下采样和超分辨力重建的图像压缩编码框架。在编码器端,为待编码的原始图像设计了多种不同的下采样模式和量化模式,通过率失真优化算法从多种模式中选择最优的下采样模式(DSM)和量化模式(QM),最后待编码图像将在选择的模式下进行下采样和JPEG编码;在解码器端,采用基于卷积神经网络的超分辨力重建算法对解码后的下采样图像进行重建。此外,所提出的框架扩展到JPEG2000压缩标准下同样有效可行。仿真实验结果表明,相比于主流的编解码标准和先进的编解码方法,提出的框架能有效地提升编码图像的率失真性能,并能获得更好的视觉效果。     

Image annotation by semi-supervised cross-domain learning with group sparsity

With the explosive growth of multimedia data in the web, multi-label image annotation has been attracted more and more attention. Although the amount of available data is large and growing, the number of labeled data is quite small. This paper proposes an approach to utilize both unlabeled data in target domain and labeled data in auxiliary domain to boost the performance of image annotation. Moreover, since different kinds of heterogeneous features in images have different intrinsic discriminative power for image understanding, group sparsity is introduced in our approach to effectively utilize those heterogeneous visual features with data of target and auxiliary domains. We call this approach semi-supervised cross-domain learning with group sparsity (S²CLGS). The strength of the proposed S²CLGS method for multi-label image annotation is to integrate semi-supervised discriminant analysis, cross-domain learning and sparse coding together. Experiments demonstrate the effectiveness of S²CLGS in comparison with other image annotation algorithms.     

基于噪声分析和稀疏正则化的图像盲复原方法

在海事搜救过程中,机载红外相机拍摄的红外图像由于直升机振动、气流扰动、高速飞行以及红外相机摆扫等因素,严重影响图像质量.根据直升机载红外相机成像特点,提出了一种基于噪声分析和稀疏正则化的图像盲复原方法.该方法首先分析了成像过程中的噪声分布,并对噪声进行预处理,再根据稀疏表达理论,用图像边缘的稀疏先验信息指导点扩散函数复原,接着通过非盲复原方法得到目标图像,将目标图像作为下一次迭代的输入图像,如此循环迭代得到清晰图像.最后,对仿真模糊图像和实拍模糊图像进行了复原实验.实验结果表明这种方法能有效改善图像质量,并且在处理实拍运动模糊图像时,相比其他复原方法效果更好.     

Image coding with robust channel-optimized trellis-codedquantization

This paper presents a wavelet-based image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. A phase scrambling stage is also used to further increase the coding performance and robustness to nonstationary signals and channels. The resilience to channel errors is obtained by optimizing the coder performance only at the level of the source encoder with no explicit channel coding for error protection. For the considered TCQ trellis structure, a general expression is derived for the transition probability matrix. In terms of the TCQ encoding rat and the channel bit error rate, and is used to design the COTCQ stage of the image coder. The robust nature of the coder also increases the security level of the encoded bit stream and provides a much more visually pleasing rendition of the decoded image. Examples are presented to illustrate the performance of the proposed robust image coder     

基于块稀疏度与自适应迭代的压缩感知方法

针对分块压缩感知算法在平滑块效应时损失了大量的细节纹理信息,从而影响图像的重构效果问题,提出了一种基于块稀疏信号的压缩感知重构算法。该算法先采用块稀疏度估计对信号的稀疏性做初步估计,通过对块稀疏度进行估算初始化阶段长,运用块矩阵与残差信号最匹配原则来选取支撑块,再运用自适应迭代计算实现对块稀疏信号的重构,较好地解决了浪费存储资源和计算量大的问题。实验结果表明,相比常用压缩感知方法,所提算法能明显减少运算时间,且能有效提高图像重构效果。     

基于振铃约束的全变差正则化图像去模糊算法

当前去模糊方法只利用图像单一的稀疏特性作为先验信息,忽略了伪边缘(如振铃瑕疵)对模糊核估计的影响,导致其去模糊性能不佳.本文充分利用复杂结构图像的先验信息,设计了振铃约束下的全变差正则化图像去模糊算法.首先,利用多分辨率图像金字塔策略建立多层图像模型,通过对比模糊图像和潜在清晰图像来获得振铃先验信息.其次,将振铃正则约...     

Image denoising using combined higher order non-convex total variation with overlapping group sparsity

Multidimensional Systems and Signal Processing - It is widely known that the total variation image restoration suffers from the stair casing artifacts which results in blocky restored images. In...     

Dissipativity-based adaptive and robust control of UPS

In this paper, we investigate the output voltage control for a three-phase uninterruptible power supply (UPS) using controllers based on ideas of dissipativity. To provide balanced sinusoidal output voltages, even in the presence of nonlinear and unbalanced loads, we first derive a dissipativity-based controller using a frequency-domain representation of system dynamics. Adaptive refinements have been added to the controller to cope with parametric uncertainties. Second, based on the first adaptive controller, we propose a controller which turns out to have the proportional-plus-integral-type structure on rotating-frame variables, but with a special design of gain matrices. A sufficient condition in terms of the design parameters is presented for this controller that guarantees stability of the desired equilibrium and robustness against parameter uncertainties. Finally, simulation and experimental results on a three-phase prototype show effectiveness and advantages of the proposed approach     

A simple and robust adaptive parasitic antenna

A novel Uda-Yagi adaptive antenna is numerically and experimentally investigated. The antenna consists of an active element and a relatively large number of parasitic elements closed on two different loads selectable by simple electronic switches. The use of fuzzy-logic based cost function and self-adaptive biological beamforming algorithms allows to obtain quite good performances both in terms of signal to interference plus noise ratio and voltage standing wave ratio. The antenna is simple, low cost, and is robust with respect to mechanical and electrical tolerances and with respect to failures of some passive elements. Experimental results on two different prototypes confirm the good performances of the proposed antenna.     

Image denoising via local and nonlocal circulant similarity

A patch based image denoising method is developed in this paper by introducing a new type of image self-similarity. This self-similarity is obtained by cyclic shift, which is called “circulant similarity”. Given a corrupted image patch, it can be estimated by incorporating circulant similarity into a weighted averaging filter. By choosing an appropriate kernel as weight function, the patch filter is implemented by circular convolution, and can be efficiently solved using fast Fourier transform. In addition, the circulant similarity can be enhanced by using nonlocal modeling. We stack the similar image patches into 3D groups, and propose a denoising scheme based on group estimation across the patches. Numerical experiments demonstrate that the proposed method with local circulant similarity outperforms much its local filtering based counterparts, and the proposed method with nonlocal circulant similarity shows very competitive performance with state-of-the-art denoising method, especially on images corrupted by strong noise.     

Brain MR image segmentation based on local Gaussian mixture model and nonlocal spatial regularization

Brain Magnetic Resonance (MR) images often suffer from the inhomogeneous intensities caused by the bias field and heavy noise. The most widely used image segmentation algorithms, which typically rely on the homogeneity of image intensities in different regions, often fail to provide accurate segmentation results due to the existence of bias field and heavy noise. This paper proposes a novel variational approach for brain image segmentation with simultaneous bias correction. We define an energy functional with a local data fitting term and a nonlocal spatial regularization term. The local data fitting term is based on the idea of local Gaussian mixture model (LGMM), which locally models the distribution of each tissue by a linear combination of Gaussian function. By the LGMM, the bias field function in an additive form is embedded to the energy functional, which is helpful for eliminating the influence of the intensity inhomogeneity. For reducing the influence of noise and getting a smooth segmentation, the nonlocal spatial regularization is drawn upon, which is good at preserving fine structures in brain images. Experiments performed on simulated as well as real MR brain data and comparisons with other related methods are given to demonstrate the effectiveness of the proposed method.