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Abstract Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging. Besides, most dehazing models are trained on synthetic data, resulting in poor generalization ability to real‐world images. To address the aforementioned problems, a semi‐supervised learning dehazing method based on the decomposition model of Osher, Solé, and Vese(The OSV model) is presented. Specifically, the OSV model is first applied to decompose the hazy image into the structure layer and texture layer, save the texture layer and dehaze for the structure layer to restore images with sharper texture and edge. Furthermore, the network adopts a semi‐supervised learning algorithm based on generative adversarial networks (GAN) to generalize better to real‐world images, which includes two branches: supervised learning and unsupervised learning. Extensive experiments indicate that the proposed method preserves the texture and edge information of images more accurately while dehazing better, and performs favourably against the advanced dehazing algorithms on both synthetic outdoor datasets and real‐world hazy images.

Euler’s elastica energy regularizer, initially employed in mathematical and physical systems, has recently garnered much attention in image processing and computer vision tasks. Due to the non-convexity, non-smoothness, and high order of its derivative, however, the term has yet to be effectively applied in 3D reconstruction. To this day, the industry is still searching for 3D reconstruction systems that are robust, accurate, efficient, and easy to use. While implicit surface reconstruction methods generally demonstrate superior robustness and flexibility, the traditional methods rely on initialization and can easily become trapped in local minima. Some low-order variational models are able to overcome these issues, but they still struggle with the reconstruction of object details. Euler’s elastica term, on the other hand, has been found to share the advantages of both the TV regularization term and the curvature regularization term. In this paper, we aim to address the problems of missing details and complex computation in implicit 3D reconstruction by efficiently using Euler’s elastica term. The main contributions of this article can be outlined in three aspects. Firstly, Euler’s elastica is introduced as a regularization term in 3D point cloud reconstruction. Secondly, a new dual algorithm is devised for the proposed model, significantly improving solution efficiency compared to the commonly used TV model. Lastly, numerical experiments conducted in 2D and 3D demonstrate the remarkable performance of Euler’s elastica in enhancing features of curved surfaces during point cloud reconstruction. The reconstructed point cloud surface adheres more closely to the initial point cloud surface when compared to the classical TV model. However, it is worth noting that Euler’s elastica exhibits a lesser capability in handling local extrema compared to the TV model.

Abstract In stereoscopic image quality assessment, human visual system has been universally taken into account to detect perceptual characteristics. A novel full‐reference stereoscopic image assessment metric by considering both monocular and binocular visual features of human visual system is proposed. In particular, a new region segmentation algorithm is firstly proposed to divide 3D images into occluded and non‐occluded regions. The just noticeable difference model is employed on the occluded regions to formulate the monocular vision, while the binocular just noticeable difference model is applied to the non‐occluded regions to reveal the binocular vision of the human visual system. In the proposed region segmentation, disparity information and Euclidean distance between stereo pairs are both adopted to solve the unstable segmentation problem of traditional methods. A new pooling strategy based on global edge features is then presented to aggregate the just noticeable difference and binocular just noticeable difference evaluation maps. In addition, some local image features as supplementary of just noticeable difference to describe visual characteristics of the human visual system are also extracted. Finally, an overall quality score is calculated based on the above‐mentioned features to measure the visual quality of distorted stereo pairs. Experimental results show that the proposed metric achieves high consistency with the human visual system, and outperforms state‐of‐the‐art algorithms on stereoscopic image quality assessment.

Variational models with second order regularizers can efficiently overcome the problems of staircasing effects caused by first order models. However, different types of second order regularizers may lead to different properties of feature preserving in restored images. In this paper, we show two variational models with second order regularizers. The first one is bounded Hessian model with Jacobian of normals, which uses bounded Jacobian of image intensity normals as regularizer, it is an extension of classical bounded Hessian (BH) model. The second one is total generalized variation model with Jacobian of normals, which is an extension to total generalized variation (TGV) model by replacing gradients in TGV with normals. The common objective is to improve feature preserving, such as edge, contrast and smoothness preservation. Additionally, their Alternating Direction Method of Multipliers (ADMM) are designed by introducing some proper auxiliary variables, Lagrange multipliers and penalty parameters to decompose the original models into some simple minimization sub-problems to solve. Extensive comparisons demonstrate that the proposed models are superior to the classical models with Hessian and TGV regularizers, especially in edge and corner preservation, smoothness, contrast enhancement. Moreover, the proposed models can be also extended to image inpainting, deblurring, and image enhancement.

Point clouds are subject to various distortions during point cloud processing missions, any of which may lead to quality degradation. Consequently, predicting point cloud quality has attracted a lot of attention. In this paper, a layered projection-based point cloud quality metric (LP-PCQM) is proposed. We layer the distorted point cloud and its original version firstly and then extract the geometry and color features of layers. The geometry feature is obtained using the projection-based method and the color features are extracted upon RGB by using the point-based method. Finally, the LP-PCQM is a weighted linear combination of an optimal subset of these pooled geometry and color features of layers. To verify the performance of LP-PCQM, we compare it with other eight metrics including both point-based metrics and projection-based metrics on the WPC, SJTU-PCQA, and ICIP2020 database respectively. Experimental results show that the proposed metric exhibits better and more robust performance.

In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Method (ALM) or Alternating Direction Method of Multiplier (ADMM) method by introducing some auxiliary variables, Lagrange multipliers, and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be easily solved via the Gauss-Seidel iterative method and generalized soft thresholding formulas with projection, respectively. Numerical experiments show that the proposed model can not only recover larger broken boundaries but can also improve segmentation efficiency, as well as decrease the dependence of segmentation on parameter tuning and initialization.

Abstract With the development of big data and cloud computing, more and more enterprises prefer to store their data in cloud and share the data among their authorized employees efficiently and securely. So far, many different data sharing schemes in different fields have been proposed. However, sharing sensitive data in cloud still faces some challenges such as achieving data privacy and lightweight operations at resource constrained mobile terminals. Furthermore, most data sharing schemes have no integrity verification mechanism, which would result in wrong computation results for users. To solve the problems, we propose an efficient and secure data sharing scheme for mobile devices in cloud computing. Firstly, the scheme guarantees security and authorized access of shared sensitive data. Secondly, the scheme realizes efficient integrity verification before users share the data to avoid incorrect computation. Finally, the scheme achieves lightweight operations of mobile terminals on both data owner and data requester sides.

Abstract The Vese-Chan model for multiphase image segmentation uses m binary label functions to construct 2 m characteristic functions for different phases/regions systematically; the terms in this model have moderate degrees comparing with other schemes of multiphase segmentation. However, if the number of desired regions is less than 2 m , there exist some empty phases which need costly parameter estimation for segmentation purpose. In this paper, we propose an automatic construction method for characteristic functions via transformation between a natural number and its binary expression, and thus, the characteristic functions of empty phases can be written and recognized naturally. In order to avoid the redundant parameter estimations of these regions, we add area constraints in the original model to replace the corresponding region terms to preserve its systematic form and achieve high efficiency. Additionally, we design the alternating direction method of multipliers (ADMM) for the proposed modified model to decompose it into some simple sub-problems of optimization, which can be solved using Gauss-Seidel iterative method or generalized soft thresholding formulas. Some numerical examples for gray images and color images are presented finally to demonstrate that the proposed model has the same or better segmentation effects as the original one, and it reduces the estimation of redundant parameters and improves the segmentation efficiency.

Image enhancement and restoration is among the most investigated topics in the field of underwater machine vision. The objective image quality assessment is a fundamental part of optimizing underwater enhancement and restoration technologies. However, most no-reference (NR) metrics are not specifically designed for underwater image quality assessment. Moreover, since the reference (undegraded) images are not available in underwater scenes, the classical full-reference (FR) metrics cannot be used to evaluate underwater image enhancement and restoration methods. In this paper, we first design an underwater image synthesis algorithm (UISA), in which depending on the real-world underwater image, we can produce a synthetic underwater image from an outdoor ground-truth image. Based on this strategy, we establish a new large-scale benchmark that contains ground-truth images and synthetic underwater images of the same scene, called synthetic underwater image dataset (SUID). Our SUID is constructed on the basis of the underwater image formation model (IFM) and characteristics of underwater optical propagation, possessing solid reliability and feasibility. The proposed SUID creates possibility for a FR evaluation of existing technologies for underwater image enhancement and restoration, which is illustrated by performing extensive experiments and quantitative analysis. The SUID is available online at: http://dx.doi.org/10.21227/agdr-y109.

Nowadays, to utilize the abundant resources of cloud computing, most enterprise users prefer to store their big data on cloud servers for sharing and utilization. However, storing data in remote cloud servers is out of user's control and exposes to lots of security problems such data availability, unauthorized access and data integrity, among which data integrity is a challenging and urgent task in cloud computing. Many auditing schemes have been proposed to check the integrity of data in cloud, but these schemes usually have some disadvantages. One is that these auditing schemes cannot check which block is corrupt when the data is not integrated. The other is that there's no efficient authenticated data structure helping to achieve accurate auditing when the data needs to update frequently. To solve the problems, we propose a public auditing scheme for dynamic big data storage in cloud computing. Firstly, we design a dynamic index table, in which no elements need to be moved in insertion or deletion update operations. Secondly, when data in cloud is not integrated, the third-party auditor can detect which block is corrupt. Finally, an authorization is employed between the third party and cloud servers to prevent denial of service attack. The theoretical analysis and the simulation results demonstrate that our scheme is more secure and efficient.

The algorithm unfolding networks with explainability of algorithms and higher efficiency of Deep Neural Networks (DNN) have received considerable attention in solving ill-posed inverse problems. Under the algorithm unfolding network framework, we propose a novel end-to-end iterative deep neural network and its fast network for image restoration. The first one is designed making use of proximal gradient descent algorithm of variational models, which consists of denoiser and reconstruction sub-networks. The second one is its accelerated version with momentum factors. For sub-network of denoiser, we embed the Convolutional Block Attention Module (CBAM) in previous U-Net for adaptive feature refinement. Experiments on image denoising and deblurring demonstrate that competitive performances in quality and efficiency are gained by compared with several state-of-the-art networks for image restoration. Proposed unfolding DNN can be easily extended to solve other similar image restoration tasks, such as image super-resolution, image demosaicking, etc.

The Euler’s elastica energy regularizer has been widely used in image processing and computer vision tasks. However, finding a fast and simple solver for the term remains challenging. In this paper, we propose a new dual method to simplify the solution. Classical fast solutions transform the complex optimization problem into simpler subproblems, but introduce many parameters and split operators in the process. Hence, we propose a new dual algorithm to maintain the constraint exactly, while using only one dual parameter to transform the problem into its alternate optimization form. The proposed dual method can be easily applied to level-set-based segmentation models that contain the Euler’s elastic term. Lastly, we demonstrate the performance of the proposed method on both synthetic and real images in tasks image processing tasks, i.e. denoising, inpainting, and segmentation, as well as compare to the Augmented Lagrangian method (ALM) on the aforementioned tasks.

This article proposes a multimode medical image fusion with CNN and supervised learning, in order to solve the problem of practical medical diagnosis. It can implement different types of multimodal medical image fusion problems in batch processing mode and can effectively overcome the problem that traditional fusion problems that can only be solved by single and single image fusion. To a certain extent, it greatly improves the fusion effect, image detail clarity, and time efficiency in a new method. The experimental results indicate that the proposed method exhibits state-of-the-art fusion performance in terms of visual quality and a variety of quantitative evaluation criteria. Its medical diagnostic background is wide.

Objective visual quality assessment specific for screen content images (SCIs) has been increasingly investigated over the years. In this paper, an effective full-reference quality evaluation model for SCIs is proposed, in which edge features in gradient domain (EFGD) are extracted for better visual perceptual representation. Unlike traditional edge feature extraction directly in the image pixel domain, all edge features in the proposed EFGD model are extracted based on the gradient map of input SCIs, including edge sharpness, edge brightness/contrast, and edge chrominance. Specifically, the gradient profile model that can well represent the spatial layout of edges is adopted to measure the edge sharpness degree. A novel computation way is reported to measure the edge brightness and contrast change between the reference and distorted SCIs, while color moments are used to account for the color chrominance variation. In addition, an adaptive weighting strategy is designed to adjust the effects of these three kinds of edge features, according to the statistical distributions of the input SCIs. Moreover, the maximum value of edge sharpness features is extracted from the test SCIs as the pooling weight to get the final image quality assessment (IQA) score. The experimental results on two commonly used SCIs databases have verified the superiorities of the EFGD model and show that the EFGD model is in more conformity with the subjective assessment results than most of the existing IQA models.

In order to remove speckle noise while preserving image features, a novel variational model for image restoration based on total curvature is proposed in this paper. Due to the characteristics of nonlinear, non-convex, and non-smooth, the proposed variational model is transformed into an alternating optimization problem through introducing a series of auxiliary variables and using the alternating direction method of multipliers. In each loop of optimization, the Fast Fourier Transform is employed to solve the involved partial differential equations numerically, and the generalized soft thresholding formula is used to solve the involved algebra equations analytically. Moreover, the projection method is applied to fulfill some related inequality constraints simply for different sub-problems, thus the computational efficiency is improved totally. The numerous experiments on synthetic and real cases are implemented to demonstrate the advantages of the proposed model in image edge and corner preserving, and to indicate the high computation efficiency of the designed algorithm through comparison with other fast algorithms.

Speckle noise removal problem has been researched under the framework of regularization-based approaches. The regularizer is normally defined as total variation (TV) that induces staircase effect. Although higher-order regularizer can conquer the staircase effect to some extent, it often leads to blurred. Considering the upper questions, the combination of first and second-order regularizer will be an effective and prior method to tackle speckle noise removal. So a variational model with hybrid TV and higher-order total curvature (TC) term is proposed in this paper, the data fidelity term is derived based on G0 distribution. In order to preserve the edge detail better, the boundary detection function is combined with the regularizer. Furthermore, the Mellin transform is used to estimate the parameters of the model. To address the speckle noise removal optimization problem, alternating direction method of multipliers (ADMM) framework is employed to design a convex numerical method for the proposed model. The numerical method can be used to update the variables flexibly as required by the hybrid regularizer. The numerous experiments were performed on both synthetic and real SAR images. Compared with some classical and state-of-theart SAR despeckling methods, experiment results demonstrate the improved performance of the proposed method, including that speckle noise can be removed effectively, and staircase effect can be prevented while preserving image feature.

Nowadays, drug–target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug–target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets. Hence, this article proposes a novel dual-network integrated logistic matrix factorization DTIs prediction scheme (Ro-DNILMF) via a knowledge graph embedding approach. This model adds prior knowledge as input data into the prediction model and inherits the advantages of the DNILMF model, which can predict under-studied drug–target interactions. Firstly, a knowledge graph embedding model based on relational rotation (RotatE) is trained to construct the interaction adjacency matrix and integrate prior knowledge. Secondly, a dual-network integrated logistic matrix factorization prediction model (DNILMF) is used to predict new drugs and targets. Finally, several experiments conducted on the public datasets are used to demonstrate that the proposed method outperforms the single base-line model and some mainstream methods on efficiency.

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

Single image dehazing and denoising models can simultaneously remove haze and noise with high efficiency. Here, the authors propose three variational models combining the celebrated dark channel prior (DCP) and total variations (TV) models for image dehazing and denoising. The authors firstly estimate the transmission map associated with depth using DCP, then design three variational models for colour image dehazing and denoising based on this estimation and the layered total variation (LTV) regulariser, multichannel total variation (MTV) regulariser, and colour total variation (CTV) regulariser, respectively. In order to improve the computation efficiency of the three models, the authors design their fast split Bregman algorithms via introducing some auxiliary variables and the Bregman iterative parameters. Numerous experiments are presented to compare their denoising effects, edge‐preserving properties, and computation efficiencies. To demonstrate the merits of the proposed models, the authors also conduct some comparisons with several existing state‐of‐the‐art methods. Numerical results further prove that the LTV‐based model is fastest, and the CTV model is the best for denoising with edge‐preserving, and it also leads to the best visually haze‐free and noise‐free images.

Thin-walled tubes have gained wide applications in aerospace, automobile and other engineering fields due to their excellent energy absorption and lightweight properties. In this study, a novel method of entropy-weighted TOPSIS was adopted to study the energy absorption characteristics of a thin-walled circular tube under axial crushing. Three types of thin-walled circular tubes, namely, aluminum (Al) tubes, carbon-fiber-reinforced plastics (CFRP) tubes and CFRP-Al hybrid thin-walled tubes, were fabricated. Quasi-static axial crushing tests were then carried out for these specimens, and their failure modes and energy absorption performance were analyzed. The CFRP material parameters were obtained through tensile, compression and in-plane shear tests of CFRP laminates. The finite element models for the quasi-static axial crushing of these three types of circular tubes were established. The accuracy of the finite element models was verified by comparing the simulation results with the test results. On this basis, the effects of the geometric dimension and ply parameters of a CFRP-Al hybrid thin-walled circular tube on the axial crushing energy absorption characteristics were studied based on an orthogonal design and entropy-weighted TOPSIS method. The results showed that Al tube thickness, CFRP ply thickness and orientation have great effect on the energy absorption performance of a CFRP-Al hybrid thin-walled circular tube, whereas the tube diameter and length have little effect. The energy absorption capability of a CFRP-Al hybrid tube can be improved by increasing the thickness of the Al tube and the CFRP tube as well as the number of ±45° plies.

The lightweight design of vehicle components is regarded as a complex optimization problem, which usually needs to achieve two or more optimization objectives. It can be firstly solved by a multi-objective optimization algorithm for generating Pareto solutions, before then seeking the optimal design. However, it is difficult to determine the optimal design for lack of engineering knowledge about ideal and nadir values. Therefore, this paper proposes a multi-objective optimization procedure combined with the NSGA-II algorithm with entropy weighted TOPSIS for the lightweight design of the dump truck carriage. The finite element model of the dump truck carriage was firstly developed for modal analysis under unconstrained free state and strength analysis under the full load and lifting conditions. On this basis, the multi-objective lightweight optimization of the dump truck carriage was carried out based on the Kriging surrogate model and the NSGA-II algorithm. Then, the entropy weight TOPSIS method was employed to select the optimal design of the dump truck from Pareto solutions. The results show that the optimized dump truck carriage achieves a remarkable mass reduction of 81 kg, as much as 3.7%, while its first-order natural frequency and strength performance are slightly improved compared with the original model. Accordingly, the proposed procedure provides an effective way for vehicle lightweight design.

Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At the same time, the fatigue life of automobile parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with the NSGA-II algorithm coupled with entropy weighted grey relational analysis (GRA) for lightweight design of the lower control arm of automobile Macpherson suspension. The dynamic load histories of the control arm were extracted through dynamic simulations of a rigid-flexible coupling vehicle model on virtual proving ground. Then, the nominal stress method was used to predict its fatigue life. Six design variables were defined to describe the geometric dimension of the control arm, while mass and fatigue life were taken as optimization objectives. The multi-objective optimization design of the control arm was carried out based on the Kriging surrogate model and NSGA-II algorithm. Aiming at the uncertainty of design variables, the reliability constraint was added to the multi-objective optimization to improve the reliability of the fatigue life of the control arm. The optimal design of the control arm was determined from Pareto solutions by entropy weighted grey relational analysis (GRA). The optimization results show that the mass of the control arm was reduced by 4.1% and the fatigue life was increased by 215.8% while its reliability increased by 7.8%. The proposed multi-objective reliability optimization method proved to be feasible and effective for lightweight design of a suspension control arm.

The implicit methods on Lie group are developed for multibody system dynamics. To avoid the violation of the displacement, velocity and acceleration constraints of the nonlinear differential-algebraic equations, the constraint-stabilized equations on Lie group are formulated. The implicit method for the Euler–Lagrange equations and the symplectic Euler method for the constrained Hamilton equations are presented. These methods can keep the stability of the displacement, velocity and acceleration constraints at the same time during the longer simulation. A three-dimensional single pendulum and a three-dimensional double pendulum are used to verify the accuracy of the constraint-stabilized Euler methods on Lie group and the orthogonality preservation of the rotation matrix.

The Euler methods on Lie group are developed for the differential–algebraic equations of multibody system dynamics with holonomic constraints. The implicit Euler method is used to solve the differential–algebraic equations as Euler–Lagrange equations on Lie group with indices 1, 2, and 3 and the case of overdetermined differential–algebraic equations mixing with configuration space. The symplectic Euler method is used to solve the differential–algebraic equations as constrained Hamilton equations on Lie group. For the discrete mapping between Lie group and Lie algebra, the canonical coordinates of the second kind for implicit first-order Crouch–Grossman Euler methods of differential–algebraic equations are used. A single pendulum and a double pendulum in the space are used to verify the accuracy of the Lie group Euler methods.

The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data. Compared with principal component analysis (PCA), SPCA can effectively reduce the dimensionality and simultaneously produce sparse principal components with sparse loadings, thus making it easy to explain the results. The experimental results indicated that the evaluation results of PCA and SPCA are consistent to a large extent. To compare the inconsistent results, we performed a subjective test, which indicated that the result of SPCA is superior to that of PCA. Most importantly, SPCA can not only compare the similarity of two craniofacial datasets but also locate regions of high similarity, which is important for improving the craniofacial reconstruction effect. In addition, the areas or features that are important for craniofacial similarity measurements can be determined from a large amount of data. We conclude that the craniofacial contour is the most important factor in craniofacial similarity evaluation. This conclusion is consistent with the conclusions of psychological experiments on face recognition and our subjective test. The results may provide important guidance for three- or two-dimensional face similarity evaluation, analysis and face recognition.

Aiming at the challenge to realize the service dynamic extension in service-oriented architecture（SOA）, a service dynamic expansion mechanism based on SOA is proposed. Services pool and service dynamic extension idea are introduced to this service dynamic expansion system model, and the services are designed into the form of services pool to be called by the system, and the system realizes the service dynamic extension by administrating the resource pool. The critical processes of the service dynamic extension rules are written in ASML（abstract state machine language）. Last spec explorer is used to test the model, validating its feasibility and validity.

The Euler–Poinaré principle is a reduced Hamilton’s principle under Lie group framework. In this article, it is applied to derive a hybrid set of dynamical equations of rigid multibody systems, which include four parts: the classical Euler–Lagrange equations of rigid bodies in their translational coordinates of mass center; Euler–Poinaré equations via orientation matrices and their related angular velocities; the constraint equations due to different joints in Cartesian coordinates and Lie groups; and the reconstruction equations between special orthogonal groups and their Lie algebras. The generalized mass matrices of dynamical equations are constant, which is computationally efficient. All the equations can be constructed systematically and can be solved easily. The construction equations can be used to design Lie group integrators of multibody system dynamics. The procedure presented in this article can be extended easily to flexible multibody systems, systems with non-holonomic constraints, and so on.

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese-Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred-PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition.

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.

The continuous and discrete Euler-Lagrangian equations with holonomic constraints are presented based on continuous and discrete Hamiltonian Principle. Using Lagrangian polynomial to interpolate state variables and Gauss quadrature formula to approximate Hamiltonian action integral, the higher order variational Galerkin integrators for multibody system dynamics with constraints and the computation procedure are given. Numerical examples are provided to show the long-time behavior of the methods proposed in this paper via comparisons with traditional Runge-Kutta methods.