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This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.

This article explores the relationships between image element pairs and label pairs and extends label diffusion to label pair diffusion for the interactive image segmentation task. Compared with label diffusion, more accurate relationships between unlabeled and labeled data can be captured on a tensor product graph (TPG) by using higher order information, and more complex interactions among image elements and finer relationships between image element pairs and label pairs are explored in label pair diffusion (LPD) process. We first establish a prior label estimation framework to measure the label pair prior probability. Then, a probability learning process on TPG is designed to smooth the label prior. The learning process is equivalent to an iterative LPD process on the original graph, which makes the proposed algorithm maintain computational efficiency. Finally, the unary label probabilities can be obtained by a total-probability-theorem-based conversion from the binary relationships. Experiments on popular segmentation data sets demonstrate the superior performance of the proposed method.

Blind image deconvolution is a fundamental task in image processing, computational imaging, and computer vision. It has earned intensive attention in the past decade since the seminal work of Fergus et al. [1] for camera shake removal. In spite of the recent great progress in this field, this paper aims to formulate the blind problem with a simpler modeling perspective. What is more important, the newly proposed approach is expected to achieve comparable or even better performance towards the real blurred images. Specifically, the core critical idea is the proposal of a pure gradient-based discriminative prior for accurate and robust blur kernel estimation. Numerous experimental results on both the benchmark datasets and real-world blurred images in various imaging scenarios, e.g., natural, manmade, low-illumination, text, or people, demonstrate well the effectiveness and robustness of the proposed approach.

User interactions are generally utilized to impose the hard constraint and estimate the label prior probability in the interactive image segmentation methods. The conventional interactive approaches cannot work well when the user inputs contain incorrect marks. The existing error tolerance methods mainly focus on the interaction itself and try to eliminate the influence of incorrect hard constraint, which however ignore the label prior estimation. This paper mainly focuses on solving the label prior estimation problem when error marks appear. The prior probability is generally defined as the matching degree between pixels and the cluster centers of marked pixels. Incorrect interaction leads to the formation of incorrect clusters. Therefore, a reliability learning model is constructed in this paper by 1) assigning smaller weighting factors to incorrect clusters, 2) assigning larger weighting factors to correct clusters with higher matching degree. Accurate label prior probability can be obtained by the weighted averaging. Then referring to the existing methods, an error-tolerant segmentation model is designed as a ratio energy function, which can both overcome the hard constraint and the label prior limitation with error marks. Experiments on the challenging Berkeley segmentation data set and Microsoft GrabCut database demonstrate the effectiveness of the proposed method.

Semantic segmentation networks usually utilize special pyramid structure after encoder or combine low-level and high-level feature maps in decoder to capture multi-scale context information, which we term them feature combination and feature connection, respectively. However, both such frameworks are less valid with the fixed geometric structure or unsuitable interim. In this paper, we advocate Dynamic Attention Network (DAN) to solve these problems. First, we design a Deformable Attention Pyramid (DAP) module to perform a self-adjustable descriptor of high-level output, which utilizes deformable function to model geometric transformation. With DAP, semantic information can be captured effectively. Second, we propose a Fusing Attention Interim (FAI) module to guide the back-propagation of long-short range information in each level of decoder. We evaluate DAN on the challenging PASCAL VOC 2012 and Cityscapes segmentation benchmarks and find that it achieves state-of-the-art results without post-processing. Our observation can be concluded that the flexible structure that possesses dynamic attention mechanism is beneficial to learn multi-scale context information.

Electrocardiography is a useful diagnostic tool for various cardiovascular diseases, such as myocardial infarction (MI). An electrocardiograph (ECG) records the electrical activity of the heart, which can reflect any abnormal activity. MI recognition by visual examination of an ECG requires an expert’s interpretation and is difficult because of the short duration and small amplitude of the changes in ECG signals associated with MI. Therefore, we propose a new method for the automatic detection of MI using ECG signals. In this study, we used maximal overlap discrete wavelet transform to decompose the data, extracted the variance, inter-quartile range, Pearson correlation coefficient, Hoeffding’s D correlation coefficient and Shannon entropy of the wavelet coefficients and used the k-nearest neighbor model to detect MI. The accuracy, sensitivity and specificity of the model were 99.57%, 99.82% and 98.79%, respectively. Therefore, the system can be used in clinics to help diagnose MI.

Super-resolution of facial images, a.k.a. face hallucination, has been intensively studied in the past decades due to the increasingly emerging analysis demands in video surveillance, e.g., face detection, verification, identification. However, the actual performance of most previous hallucination approaches will drop dramatically when a very low-res tiny face is provided, due to the challenging multimodality of the problem as well as lack of an informative prior as a strong semantic guidance. Inspired by the latest progress in deep unsupervised learning, this paper focuses on tiny faces of size 16 × 16 pixels, hallucinating them to their 8 × upsampling versions by exploring the potentials of Wasserstein generative adversarial networks (WGAN). Besides a pixel-wise L2 regularization term imposed to the generative model, it is found that our advocated autoencoding generator with both residual and skip connections is a critical component for WGAN representing the facial contour and semantic content to a reasonable precision. With the additional Lipschitz penalty and architectural considerations for the critic in WGAN, the proposed approach finally achieves state-of-the-art hallucination performance in terms of both visual perception and objective assessment. The cropped CelebA face dataset is primarily used to aid the tuning and analysis of the new method, termed as tfh-WGAN. Experimental results demonstrate that the proposed approach not only achieves realistic hallucination of tiny faces, but also adapts to pose, expression, illuminance and occluded variations to a great degree.

Single-image nonparametric blind super-resolution is a fundamental image restoration problem yet largely ignored in the past decades among the computational photography and computer vision communities. An interesting phenomenon is observed that learning-based single-image super-resolution (SR) has been experiencing a rapid development since the boom of the sparse representation in 2005s and especially the representation learning in 2010s, wherein the high-res image is generally blurred by a supposed bicubic or Gaussian blur kernel. However, the parametric assumption on the form of blur kernels does not hold in most practical applications because in real low-res imaging a high-res image can undergo complex blur processes, e.g., Gaussian-shaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories. The paper is mainly motivated by one of our previous works: Shao and Elad (in: Zhang (ed) ICIG 2015, Part III, Lecture notes in computer science, Springer, Cham, 2015). Specifically, we take one step further in this paper and present a type of adaptive heavy-tailed image priors, which result in a new regularized formulation for nonparametric blind super-resolution. The new image priors can be expressed and understood as a generalized integration of the normalized sparsity measure and relative total variation. Although it seems that the proposed priors are simple, the core merit of the priors is their practical capability for the challenging task of nonparametric blur kernel estimation for both super-resolution and deblurring. Harnessing the priors, a higher-quality intermediate high-res image becomes possible and therefore more accurate blur kernel estimation can be accomplished. A great many experiments are performed on both synthetic and real-world blurred low-res images, demonstrating the comparative or even superior performance of the proposed algorithm convincingly. Meanwhile, the proposed priors are demonstrated quite applicable to blind image deblurring which is a degenerated problem of nonparametric blind SR.

Many interactive image segmentation methods design energy functions based on the local relationship of neighboring pixels, which is insufficient at capturing the abundant information of an image, and these methods are susceptible to many problems, such as sensitivity to seeds and under-segmentation of boundaries. To solve these problems, this paper explores utilizing the global relationship for interactive image segmentation. To effectively obtain the global information of the image, we first propose a robust affinity diffusion (RAD) method to propagate the local affinity graph. Compared with the existing diffusion-based approaches, the advantage of RAD is that it can converge to an effective limit value, which makes the diffusion process more computationally efficient and easier to control. The segmentation model is then constructed based on this convergent global graph. To efficiently utilize the global information, the energy function is designed by the multiplication of the global affinity matrix and a prior probability vector. The use of global information can significantly improve the segmentation performance. Experiments on challenging data sets demonstrate that RAD can obtain better results than state-of-the-art methods.

The performance of conventional interactive image segmentation methods is strongly affected by seed quantity and position, and it is difficult for them to maintain global data coherence due to the bias that is caused by limited interactions. Furthermore, the pixel-level relationships in these methods are too local to capture long-range connectivity cues, which often causes them to obtain under-segmented results. To solve these problems, this paper proposes an interactive segmentation method that is based on likelihood diffusion and perceptual learning. The diffusive likelihood strategy is proposed for accurately estimating the prior label probability from limited user inputs. Superpixel-level grouping cues are utilized to enforce continuity during the segmentation process. The geometrical adjacency and long-range grouping cues are fused in the proposed framework to ensure that the segmentation results maintain proximity and continuity. The final results can be obtained by applying a joint optimization technique to solve a pair of sub-module functions. Experiments on the Berkeley segmentation data set and the Microsoft GrabCut database demonstrate that the proposed method outperforms state-of-the-art methods.

Multiple image variations occur in natural face images, such as the changes of pose, illumination, occlusion and expression. For non-specific variations based face recognition, learning effective features is an important research topic. Subspace learning is a widely used face recognition technique; however, numerous subspace analysis methods do not fully utilize the prior information of facial variations. Tensor-based multilinear subspace analysis methods can take advantage of the prior information, but they need to be further improved. With respect to a single facial variation, we observe that the image samples belonging to the same variation-state but different classes tend to cluster together, whereas those belonging to different variation-states but the same class tend to remain separate. This is adverse to classification. In this paper, motivated by the idea of charge law, “like charges repulsion and opposite charges attraction”, in which like and opposite charges are regarded as same and different variation-states, respectively, we propose a non-specific variations based discriminant analysis (NVDA) criterion. It searches for an optimal discriminant subspace in which samples belonging to same variation-state but different classes are separable, whereas those belonging to different variation-states but same class cluster together. We then propose a novel face recognition approach called non-specific variations based multi-subspace analysis (NVMSA), which serially utilizes NVDA criterion to learn multiple discriminant subspaces corresponding to different variations. In the proposed approach, we design a strategy to select the serial calculation order of variations and provide a rule to choose projection vectors with favorable discriminant capabilities. Furthermore, we formulate the locally statistical orthogonal constraints for the multiple subspaces learning to remove the local correlation of discriminant features obtained from multiple variations. Experiments on the AR, Weizmann, PIE and LFW databases demonstrate the effectiveness and efficiency of the proposed approach.

Low rank methods have shown to provide excellent denoising performance, of which weighted nuclear norm minimization (WNNM) is particularly effective. It assigns different weights to different singular values. However, it has limitations in three aspects, namely, no consideration of the noise effect on the similarity measure, a fixed feedback proportion of method noise, and an inflexible number of iterations for each image. In this paper, a general denoising framework based on WNNM is proposed that offers three strategies to mitigate the above drawbacks. The first strategy is to perform a coarse prefiltering on noisy patches before patch matching. The second is to adaptively feed different percentages of method noise back according to the additive noise levels. And the last strategy is to apply a stopping criterion based on Pearson's correlation coefficient during iteration. Experimental results demonstrate the efficiency of the proposed approach. Performing a coarse prefiltering on noisy patches before patch matching.Feeding different percentages of method noise back according to the additive noise levels.Applying a stopping criterion based on Pearson's correlation coefficient during iteration.

Most existing color face feature extraction methods need to perform color space transformation, and they reduce correlation of color components on the data level that has no direct connection with classification. Some methods extract features from R, G and B components serially with orthogonal constraints on the feature level, yet the serial extraction manner might make discriminabilities of features derived from three components distinctly different. Multi-set feature learning can jointly learn features from multiple sets of data effectively. In this paper, we propose two novel color face recognition approaches, namely multi-set statistical uncorrelated projection analysis (MSUPA) and multi-set discriminating uncorrelated projection analysis (MDUPA), which extract discriminant features from three color components together and simultaneously reduce the global statistical and global discriminating feature-level correlation between color components in a multi-set manner, respectively. Experiments on multiple public color face databases demonstrate that the proposed approaches outperform several related state-of-the-arts. We propose a multi-set statistical uncorrelated projection analysis approach.We define a supervised correlation that is the discriminating correlation.We propose a multi-set discriminating uncorrelated projection analysis approach.Performance of our approaches is demonstrated on multiple color face databases.

In this paper, we investigate a novel learning-based method for dynamic scene deblurring. Since the inference model is formulated as an encoder-decoder, the core task has turned to learning blur-invariant hidden features from the blurred images to a great degree. To achieve state-of-the-art results in terms of both deblurring accuracy and efficiency, a simple, robust and computationally efficient deep auto-encoder is developed tailored specifically for blind deblurring, which is learned in an adversarial fashion based on use of the recent Wasserstein generative adversarial networks. Thanks to the designed framework, the new model has shown comparable or superior performance both qualitatively and quantitatively to existing state-of-the-art methods. What is more important, instead of exploiting the multi-scale strategy as previous methods, our model is just single-scale capable of achieving 6 times more efficiency than the closest competitor by Nah et al. [1], which is a more complex multi-scale deep method.

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.

This paper proposes a new variational model for deblurring low-resolution images, a.k.a. single image nonparametric blind super-resolution. In specific, a type of new adaptive heavy-tailed image priors are presented incorporating both the model discriminativeness and effectiveness of salient edge pursuit for accurate and reliable blur kernel estimation. With the assistance of appropriate non-blind super-resolution approaches, nonparametric blind super-resolution can be cast as a regularized functional minimization problem. An efficient numerical algorithm is derived by harnessing the alternating direction method of multipliers as well as the conjugate gradient method, with which alternatingly iterative estimations for kernel and image are finally implemented in a multi-scale manner. Numerous experiments are conducted along with comparisons made among the proposed approach and two recent state-of-the-art ones, demonstrating that the proposed approach is able to better deal with low-resolution images which are blurred by various possible kernels, e.g., Gaussianshaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories.

This demo designs and implements a system called FMQO that can support multiple query optimization in federated RDF systems. Given a set of queries posed simultaneously, we propose a heuristic query rewriting-based approach to share the common computation during evaluation of multiple queries. Furthermore, we propose an efficient method to use the interconnection topology between SPARQL endpoints to filter out irrelevant sources and join intermediate results during multiple query evaluation. The experimental studies over both real federated RDF datasets show that the demo is effective, efficient and scalable.

Diffusion technique is powerful for semi-supervised image segmentation since the geometry of the data manifold can be captured by the affinity propagation. Conventional diffusion methods focus on single label, which however ignore interactions among labels. This workstudies a generalized diffusion framework that considers label group for diffusion (LGD). The proposed framework can effectively capture the interactions among image elements via tensor product graph (TPG). A multivariate affinity framework is proposed to learn on higher-order TPG. Label pair diffusion algorithm is naturally derivedfrom the framework by considering second-order affinity (LG(2)D). We theoretically show that conventional label diffusion is the simplest case of the proposed framework (LG(1)D). Extensive experiments on image segmentation and image pair co-segmentation datasets demonstrate the superior performance of the proposed framework.

The tensor factorization techniques preserve the intrinsic structure information better than the matrix factorization technique. However, the outliers on each mode are usually non-independent and identically distributed (non-i.i.d) and the incoherence is non-uniform. The existing matrix factorization methods cannot deal with the non-i.i.d outliers and the non-uniform incoherence well. Although Kronecker-Decomposable sparse dictionary learning (KDSDL) model has shown effectiveness in dealing with the non-uniform incoherence, the non-i.i.d outliers and the correlation between different mode are still not considered. To address these problems, we propose a graph regularized Bayesian tensor factorization based on KDSDL model. First, to consider the non-i.i.d noise in real scenario, we embed the mixture of outliers in each mode through KDSDL model; Second, to capture the correlation between different modes, a novel graph sparsity-inducing regularization is proposed. The experiments on tensor data with complex outliers demonstrate the superiority of the proposed methods over the existing state-of-the-art methods.

The development of green buildings industry is significant to achieve global sustainable development goals. In China, the level of green buildings industry has great development space. However, the adoption of green buildings is often hindered by complicated behavior of different stakeholders to some extent. This research aims at analyzing the complex relationships among stakeholders’ green behavior and exploring the driving-paths for green buildings industry based on the network analysis. The green behavior system of six major stakeholders was constructed based on the previous literatures. Then, whole-network analysis explores the visual relationships through questionnaires data, while ego-network analysis presenting by upwards and downwards structure mines the vital driving-paths for green buildings industry. Finally the study reveals, (i) the end-users are critical to the development direction of green buildings industry; (ii) when issuing the incentive policies for green buildings, government should consider creating trade-offs between the incremental cost and profit; (iii) as the main body of green innovation, designers and suppliers are overdependent on governmental incentives. Strategically, these findings provide a visual analysis model for stakeholders to carry out green strategies and transformation upgrading.

In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. The experimental results on medical and texture images demonstrate the advantages of the proposed method.

This paper proposes a regularized negative log-marginal-likelihood minimization method for motion blur-kernel estimation, which is the core problem of blind motion deblurring. In contrast to existing approaches, the proposed method treats the blur-kernel as a deterministic parameter in a directed graphical model wherein, the sharp image is sparsely modeled by using a three-layer hierarchical Bayesian prior and the inverse noise variance is supposed distributed to the Gamma hyper-prior. By borrowing the ideas of mean filed approximation and iteratively reweighted least squares, the posterior distributions of the sharp image, the inverse noise variance and the hyper-parameters involved in the image prior, as well as the deterministic model parameters including the motion blur-kernel and those involved in the hyper-priors, are all estimated automatically for each blind motion deblurring problem. It is worthy to note that, the new approach relies on a strict minimization objective function, and learns a more adaptive sparse image prior while with considerably less implementation heuristics compared with existing motion blur-kernel estimation approaches. Experimental results on both benchmark and real-world motion blurred images demonstrate that the proposed method has achieved state-of-the-art or even better performance than the current blind motion deblurring approaches in terms of the image deblurring quality. The results also show that the proposed approach is robust to the size of the motion blur-kernel to a great extent. A new motion blur-kernel estimation method is proposed for blind image deblurring.The new method is formulated in a unified and rigorous optimization perspective.Sparse image priors are learned adaptively for each blind deblurring problem.The noise variance is automatically estimated unlike state-of-the art VB methods.The method achieves better performance in terms of deblurring effectiveness

The linear heat source (LHS) method is an effective way to realize large-range and distributed monitoring of seepage. Research on this method, however, fails to consider the influence of seepage direction. Based on the seepage mechanics and heat transfer theory, this article sets up a mathematical model to describe the heat dissipation process of LHS in porous media. On such basis, the finite element method was adopted to study the influence of seepage velocity and seepage direction on the law of heat dissipation of LHS. The results showed that the heat dissipation process of LHS meet well with Newton’s Law of Cooling. Its cooling velocity was in positive correlation with the seepage velocity and infiltration angle. Hence, when the seepage direction is unknown, adopting this method for seepage identification is uncertain, may leads to misjudgment. At the end of the paper, the authors discuss a solution to this problem.

This paper aims to exploit the full potential of gradient-based methods, attempting to explore a simple, robust yet discriminative image prior for blind deblurring. The specific contributions are three-fold: Above all, a pure gradient-based heavy-tailed model is proposed as a generalized integration of the normalized sparsity and the relative total variation. On the second, a plug-and-play algorithm is deduced to alternatively estimate the intermediate sharp image and the nonparametric blur kernel. With the numerical scheme, image estimation is simplified to an image smoothing problem. Lastly, a great many experiments are performed accompanied with comparisons with state-of-the-art approaches on synthetic benchmark datasets and real blurry images in various scenarios. The experimental results show well the effectiveness and robustness of the proposed method.

We propose a convolutional sparse coding (CSC) for super resolution (CSC-SR) algorithm with a joint Bayesian learning strategy. Due to the unknown parameters in solving CSC-SR, the performance of the algorithm depends on the choice of the parameter. To this end, a coupled Beta-Bernoulli process is employed to infer appropriate filters and sparse coding maps (SCM) for both low resolution (LR) image and high resolution (HR) image. The filters and the SCMs are learned in a joint inference. The experimental results validate the advantages of the proposed approach over the previous CSC-SR and other state-of-the-art SR methods.

In this paper, we propose a video enhancement method using temporal-spatial total variation Retinex and luminance adaption. To utilize the temporal information between video frames, we construct a illumination data fidelity term and propose an temporal-spatial total variation model for Retinex. In order to further enhance the contrast of video frames, we use the adaptive Gamma correction with weighting distribution as a post processing step. Thus, the proposed method is able to enhance video frames' contrast while producing coherent illumination. Experimental results demonstrate the efficiency of the proposed method.

The nonparametric blur-kernel estimation, using either single image or multi-observation, has been intensively studied since Fergus et al.'s influential work (ACM Trans Graph 25:787---794, 2006). However, in the current literature there is always a gap between the two highly relevant problems; that is, single- and multi-shot blind deconvolutions are modeled and solved independently, lacking a unified optimization perspective. In this paper, we attempt to bridge the gap between the two problems and propose a rigorous and unified minimization function for single/multi-shot blur-kernel estimation by coupling the maximum-a-posteriori (MAP) and variational Bayesian (VB) principles. The new function is depicted using a directed graphical model, where the sharp image and the inverse noise variance associated with each shot are treated as random variables, while each blur-kernel, in difference from existing VB methods, is just modeled as a deterministic parameter. Utilizing a universal, three-level hierarchical prior on the latent sharp image and a Gamma hyper-prior on each inverse noise variance, single/multi-shot blur-kernel estimation is uniformly formulated as an $${\varvec{\fancyscript{l}}}_{{0.5}}$$l0.5-norm-regularized negative log-marginal-likelihood minimization problem. By borrowing ideas of expectation-maximization, majorization-minimization, and mean field approximation, as well as iteratively reweighted least squares, all the unknowns of interest, including the sharp image, the blur-kernels, the inverse noise variances, as well as other relevant parameters are estimated automatically. Compared with most single/multi-shot blur-kernel estimation methods, the proposed approach is not only more flexible in processing multiple observations under distinct imaging scenarios due to its independence of the commutative property of convolution but also more adaptive in sparse image modeling while in the meanwhile with much less implementational heuristics. Finally, the proposed blur-kernel estimation method is naturally applied to two low-level vision problems, i.e., camera-shake deblurring and nonparametric blind super-resolution. Experiments on benchmark real-world motion blurred images, simulated multiple-blurred images, as well as both synthetic and realistic low-resolution blurred images are conducted, demonstrating the superiority of the proposed approach to state-of-the-art single/multi-shot camera-shake deblurring and nonparametric blind super-resolution methods.

This paper presents an interactive image segmentation approach where the segmentation problem is formulated as a probabilistic estimation manner. Instead of measuring the distances between unseeded pixels and seeded pixels, we measure the similarities between pixel pairs and seed pairs to improve the robustness to the seeds. The unary prior probability of each pixel belonging to the foreground F and background B can be effectively estimated based on the similarities with label pairs (F, F),(F, B),(B, F) and (B, B). Then a likelihood learning framework is proposed to fuse the region and boundary information of the image by imposing the smoothing constraint on the unary potentials. Experiments on challenging data sets demonstrate that the proposed method can obtain better performance than state-of-the-art methods.

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.

In order to attain “peak load shifting”, the main problem is to solve operational optimal control of heat storage boiler in this paper. Making the electric heat storage boiler the main object of the thesis, considering constraints of electric heat storage boilers such as heat storage capacity, power and temperature, etc., the control strategy of electric heat storage boiler based on multi-agent is proposed through incorporating the ideas of multi-agent. With simulating the heating system of the electric heat storage boilers and regarding a single electric heat-storage boiler as a single-agent, the load demand model of electric heat storage boiler is built and multiple electric boilers can communicate and coordinate mutually, thus the optimization control of electric heat storage boiler is realized. Simulation for a specific case is conducted, and the results indicate that the proposed control strategy based on multi-agent can effectively achieve “peak load shifting” by the electric heating units, reducing the difference between peak and valley.

This paper introduces a fast blind deconvolution strategy for image deblurring by modifying a recent natural image model, i.e., the total generalized variation (TGV), which aims at reconstructing an image with higher-order smoothness as well as sharp edges. But, when it turns to the blind issue, as demonstrated either empirically or theoretically by a few previous blind deblurring works, natural image models including TGV are more often than not inclined to trivial solutions, e.g., the delta blur kernel and the input blurred images. Inspired by the discovery, a simple, yet effective modifying strategy is applied to the second-order TGV, leading to a novel l 0 –l 1 -norm-based image regularization adaptable to the blind deblurring problem. Then, a numerical algorithm is deduced with O(NlogN) complexity, via borrowing ideas of the operator splitting method, the augmented Lagrangian, and also the fast Fourier transform (FFT). Experimental results on both synthetic and real color blurred images demonstrate the superiority or comparable performance of the new approach to state-of-the-art ones, in terms of deblurring accuracy and efficiency.

Learning-based methods have been the mainstream in single image super-resolution (SISR) over the past decade or so. This paper, however, returns to the reconstruction perspective to SISR, and studies the possibility of achieving competitive or even better SISR performance compared with state-of-the-art learning-based approaches in the literature. Specifically, through the (fast) iterative shrinkage-thresholding algorithm, a general, simple, yet accurate SISR framework is proposed by embedding off-the-shelf image denoising algorithms into the reconstruction process rather than struggling to develop advanced image priors. Numerous experiments on the benchmark dataset are performed accompanied by comparisons against a few recent learning-based methods, e.g., A+, SRCNN, etc. The results have demonstrated well the rationality and superiority of the proposed framework in terms of the super-resolution accuracy as image filtering schemes such as BM3D, WNNM, and other possible candidates, are used. Note that, learning-based filtering schemes can be also plugged into our framework, which hence leads to hybrid reconstruction/learning-based SISR approaches, demonstrating the flexibility as well as the potential of the proposed framework.

In this paper, we studied the current carrying capacity of solder joints in the Copper Column Grid Array (CuCGA) by means of finite element method (FEM). The current carrying capacity of solder joints can be reflected by the peak value of the temperature and current density. And the solder fillets were the critical region in the entire CuCGA. Therefore, the paper mainly studied the influence of the shape-parameters for solder joints and wirings to the peak value of the temperature and current density. The results were as follows, the peak value of the current density of solder fillets occurred in the geometric discontinuity region connected the solder fillets and wirings. And the peak value of the temperature of soldering fillets occurred in the geometric discontinuity region connected the solder fillets and the copper columns. With the increase of height for solder columns, the peak value of current density was constant, while the peak value of temperature decreased slightly. With the increase of diameter for solder columns, both the peak value of current density and the temperature decreased. With the increase of climbing height for soldering, the current density in the solder joints decreased. With the increased of minimum width of wiring, both the peak value of temperature and the peak value of current density appeared the trend of decreased.

We describe a new method for simultaneous image denoising and level set-based active contour segmentation using multidimensional features. We consider an image to be a surface embedded in a Riemannian manifold. By defining a metric in the embedded space, which in our case includes multidimensional image features as well as a level set-based active contour model, a minimization problem in the image space can be obtained through the Polyakov action framework. The resulting minimization problem is solved with a dual algorithm for efficiency. Benefits of this new method include the fact that it is independent of any artificial “running” parameters, and experiments using both synthetic and real images show that the method is robust with respect to noise and blurry object boundaries.

We introduce a new graph polynomial that encodes interesting properties of graphs, for example, the number of matchings and the number of perfect matchings. Most importantly, for bipartite graphs the polynomial encodes the number of independent sets (#BIS). We analyze the complexity of exact evaluation of the polynomial at rational points and show that for most points exact evaluation is #P-hard (assuming the generalized Riemann hypothesis) and for the rest of the points exact evaluation is trivial. We conjecture that a natural Markov chain can be used to approximately evaluate the polynomial for a range of parameters. The conjecture, if true, would imply an approximate counting algorithm for #BIS, a problem shown, by [Dyer et al. 2004], to be complete (with respect to, so called, AP-reductions) for a rich logically defined sub-class of #P. We give a mild support for our conjecture by proving that the Markov chain is rapidly mixing on trees. As a by-product we show that the "single bond flip" Markov chain for the random cluster model is rapidly mixing on constant tree-width graphs.