For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high efficiency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution.

Craniofacial superimposition is a crucial forensic science technique to identify human remains by matching skulls to facial images. However, this task is challenging due to significant morphological differences between skulls and faces, limited paired samples, and high data dimensionality. We proposed a geometric feature-driven metric learning method for craniofacial superimposition to address these issues. Firstly, we extracted geometric features, including depth, curvature, and elevation of 3D craniofacial data, to generate 2D maps of structured representations enriched with geometric details. Next, we novelly designed a Triplet Network for geometric feature-driven metric learning, which leverages triplet loss to learn discriminative embeddings and effectively handle the limited paired data problem. By incorporating the Sinkhorn Distance as an additional constraint, we aligned the skull and face data distributions, enhancing the matching precision. We conducted extensive experiments on a 3D craniofacial dataset, achieving a maximum accuracy of 99.45% on curvature maps, surpassing state-of-the-art methods. Our code will be available after publication at https://github.com/Lqd-js/cranial-superimposition.

3D facial reconstruction plays a crucial role in virtual reality and entertainment. Impressive rendering and animation effects have been achieved through recent advances. Existing Gaussian avatar models generate diverse expressions, but the corresponding texture changes are not so satisfactory, and their geometric structure often falls short. In response to these challenges, we propose a 3D head avatar with remarkable geometry, which can control expression variations and enable facial texture information to change along with expressions. To achieve this effect, we combine the 2D Gaussian field with the facial parametric model, use the mesh to drive Gaussian field, design a fine-tuning field for the mouth area to fit distorted expressions, and simultaneously design a color variation module to simulate changes in facial wrinkles and skin. Experiments demonstrate that our avatar model exhibits excellent performance in both appearance details and geometric shapes.

Simulation speed is crucial for virtual reality simulators that simulate real-time cutting of deformable objects with haptic feedback, such as surgical simulators. This type of simulator combines visual feedback and haptic feedback, and therefore can be considered as a type of Multimedia Applications. To increase simulation speed, improvements are made in this paper to a previous deformable cutting method which divides a deformable object’s surface mesh into an interface mesh (including exterior surface mesh and interior surface mesh between different materials) constructed on a fine level linked voxel grid and a cut surface mesh constructed on a coarse level linked voxel grid. Our method changes the fine level linked voxel grid from a uniform grid to an octree. The algorithms for constructing and incrementally updating the object surface mesh and the collision proxy (an approximation of the object surface mesh used for collision processing) are changed accordingly. A new algorithm is proposed to resolve inconsistencies between partially cut and fully cut parts using visibility tests. Simulation tests show that our proposed method can moderately increase simulation speed during cutting and reduce CPU memory usage with almost imperceptible reductions in rendering qualities.

The Alternating Direction Method of Multipliers (ADMM) has gained significant attention across a broad spectrum of machine learning applications. Incorporating the over-relaxation technique shows potential for enhancing the convergence rate of ADMM. However, determining optimal algorithmic parameters, including both the associated penalty and relaxation parameters, often relies on empirical approaches tailored to specific problem domains and contextual scenarios. Incorrect parameter selection can significantly hinder ADMM's convergence rate. To address this challenge, in this paper we first propose a general approach to optimize the value of penalty parameter, followed by a novel closed-form formula to compute the optimal relaxation parameter in the context of linear quadratic problems (LQPs). We then experimentally validate our parameter selection methods through random instantiations and diverse imaging applications, encompassing diffeomorphic image registration, image deblurring, and MRI reconstruction.

3D face shape completion is a necessary pre-process for various facial applications as they are often mutilated due to the acquiring environment or occlusion. However, it is challenging to ensure identity consistency when completing faces with large missing regions. Therefore, we introduce craniofacial information to supervise the completion of face shapes. Firstly, a novel dual encoder-decoder structure for face depth image inpainting is constructed by combining dilated convolution and the coherent semantic attention mechanism, guaranteed to generate smooth results with complete semantic information even in the presence of large missing regions in the face model. Then, we innovatively design a craniofacial superimposition module to determine the probability that the inpainted face and corresponding skull come from the same person, constraining the inpainting network to learn identity consistency information. Finally, extensive experimental results show that our method can effectively complete 3D face shapes containing large arbitrary missing regions while guaranteeing identity consistency.

Facial expressions are an essential part of human emotional communication, and micro-expressions (MEs), as transient and imperceptible non-verbal signals, can potentially reveal real human emotions. However, subtle motion variations, limited and unbalanced samples make micro-expression recognition (MER) challenging. In this paper, we design a novel dual-branch learning framework of multi-level flow-driven attention for micro-expression recognition (MFDAN), which innovatively integrates optical flow prior to guide the attention learning in the image encoding branch, enabling the model to focus on the most discriminative facial regions for subtle motion patterns. Firstly, we extract optical flow information by an optical flow encoding module. Then, in the image coding module, we construct a Transformer structure containing an optical flow-driven attention mechanism, which can effectively locate the interest region of micro-expressions in the image according to the position information of optical flow to capture more sensitive and fine-grained micro-expressions. By interoperating prior knowledge with data learning, and introducing the Dropkey operation and Focal Loss, our method can handle subtle micro-expression features on small imbalanced datasets. Through extensive experiments on three independent datasets and a composite database, including SMIC-HS, SAMM, and CASME II, robust leave-one-subject-out (LOSO) evaluation results show that our method outperforms state-of-the-art methods especially on the composite database.

This article proposes a two-stage image segmentation method based on the MS model, aiming to enhance the segmentation accuracy of images with complex structure and background. In the first stage, in order to obtain the smooth approximate solution of the image by minimizing the energy functional, an anisotropic regularization term formed by the combination of the gradient operator and an adaptive weighted matrix is introduced. Different weights in both horizontal and vertical directions can be provided by the adaptive weighting matrix according to the gradient information, so that the curve diffuses along the directions of local feature tangents of the objects. In addition, information irrelevant to the image target can be filtered out by the adaptive weighting matrix, thus reducing the interference of complex background. The alternating direction method of multipliers (ADMMs) is employed to solve the convex optimization problem in the first stage. In the second stage, the smoothed image obtained in the first stage is segmented by the deep learning method. By comparing with some traditional methods and deep learning methods, the results demonstrate that not only has good perceptual quality been achieved by this segmentation method, but also superior evaluation metrics have been obtained.

Bearing fault diagnosis of electrical equipment has been a popular research area in recent years because there are often some faults during continuous operation in production due to the harsh working environment. However, the traditional fault signal processing methods rely on highly expert experience, and some parameters are difficult to be optimized by machine-learning methods. Thus, the satisfactory recognition accuracy of fault diagnosis cannot be achieved in the above methods. In this article, a new model based on the spiking neural network (SNN) is proposed, which is called deep the spiking residual shrinkage network (DSRSN) for bearing fault diagnosis. In the model, attention mechanisms and soft thresholding are introduced to improve the recognition rate under a high-level noise background. The higher recognition accuracy is obtained in the proposed model which is tested on the fault signal dataset under different noise intensities. Meanwhile, the training time is about treble as fast as the training time of the artificial neural network, which is reflecting the high efficiency of SNN.

In hyperspectral images (HSIs), both local and non-local features play crucial roles in classification tasks. Vision Transformer (VIT) can extract non-local features through attention mechanisms, while Convolutional Neural Networks (CNN) excel at handling local components. However, in traditional dual-branch models based on VIT and CNN, there is a lack of interaction during feature processing, leading to potential compatibility issues when merging the two types of features. In this article, we propose HyperSINet, a Synergetic Interaction Network that combines VIT and CNN to establish interaction between the two branches, enabling mutual compensation between local and non-local features during the training process and ultimately enhancing the performance of classification tasks. Specifically, we devise a pair of interactors, namely Conv2Trans and Trans2Conv, which serve as intermediaries between the two branches, enabling the VIT branch to refine its local details, while allowing the CNN branch to process larger receptive field non-local features. Typical feature Maps are implemented to visualize the function of the interactors. Furthermore, within the VIT branch, a VIT Encoder with the local mask is developed to strike a balance between emphasizing non-local features and preserving local details, while a lightweight CNN block is designed to process spectral and spatial features in the CNN branch. Extensive experiments conducted on four real-world datasets demonstrate that, under a reasonable count of parameters, HyperSINet surpasses several current state-of-the-art methods.

As a significant geometric feature of 3D point clouds, sharp features play an important role in shape analysis, 3D reconstruction, registration, localization, etc. Current sharp feature detection methods are still sensitive to the quality of the input point cloud, and the detection performance is affected by random noisy points and non-uniform densities. In this paper, using the prior knowledge of geometric features, we propose a Multi-scale Laplace Network (MSL-Net), a new deep-learning-based method based on an intrinsic neighbor shape descriptor, to detect sharp features from 3D point clouds. First, we establish a discrete intrinsic neighborhood of the point cloud based on the Laplacian graph, which reduces the error of local implicit surface estimation. Then, we design a new intrinsic shape descriptor based on the intrinsic neighborhood, combined with enhanced normal extraction and cosine-based field estimation function. Finally, we present the backbone of MSL-Net based on the intrinsic shape descriptor. Benefiting from the intrinsic neighborhood and shape descriptor, our MSL-Net has simple architecture and is capable of establishing accurate feature prediction that satisfies the manifold distribution while avoiding complex intrinsic metric calculations. Extensive experimental results demonstrate that with the multi-scale structure, MSL-Net has a strong analytical ability for local perturbations of point clouds. Compared with state-of-the-art methods, our MSL-Net is more robust and accurate.

Landmark localization plays a significant role in craniofacial registration, reconstruction, and authentication. The key challenges for localizing landmarks on point cloud craniofacial models include irregular structures, non-uniform densities, and uncertain local regions. In this paper, we propose an end-to-end regression network that can directly estimate craniofacial landmarks on point cloud models. The proposed network utilizes edge convolution to extract local features and pooling layers to aggregate global features. It realizes the end-to-end regression for landmark localization. Experimental results demonstrate that our method is robust on point clouds with sparse and unevenly distributed sampling. It can produce accurate, controllable, and efficient 3D landmarks.

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.

Recently, there has been growing interest in hyperspectral images (HSIs) classification tasks, with both Graph Neural Networks (GNN) and Convolutional Neural Networks (CNN) proving to be effective means of analysis. GNN can better capture the spatial structure of HSIs in large target irregular regions through superpixel segmentation, while CNN can refine classification tasks by processing pixel-level features in small target regular regions. However, neither GNN nor CNN models alone can simultaneously consider superpixel-level and pixel-level features to cover both large and small target regions. To fully utilize the strengths of GNN and CNN, we propose a novel model called the Graph-Polarized Fusion Network (GPF). The GPF consists of two branches: the Fusion Graph Neural Network (FGNN) classifier in the GNN branch conducts feature learning on large, irregular target regions using both Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as feature extraction operators. The features are integrated using three aggregators, namely Min, Max, and Weighted Add, followed by updating the nodes through 2D convolutional layers. The Polarized Neural Network (PNN) classifier of the CNN branch primarily works on small, target regular regions using Polarized Self-Attention (PSA) to conduct high-resolution processing on the two dimensions of space and channel without increasing time loss. Additionally, GPF employs residual connections to extract features from long distances and multi-angles. It also uses weighted fusion to integrate the superpixel-level and pixel-level features obtained from the two branches. Rigorous experiments on five real datasets demonstrate that GPF can fully mine the latent features of HSIs, achieving competitive results compared with other state-of-the-art methods.

With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we make one of the first attempts developing a bitstream-based no-reference (NR) model for perceptual quality assessment of point clouds without resorting to full decoding of the compressed data stream. Specifically, we first establish a relationship between texture complexity and the bitrate and texture quantization parameters based on an empirical rate-distortion model. We then construct a texture distortion assessment model upon texture complexity and quantization parameters. By combining this texture distortion model with a geometric distortion model derived from Trisoup geometry encoding parameters, we obtain an overall bitstream-based NR point cloud quality model named streamPCQ. Experimental results show that the proposed streamPCQ model demonstrates highly competitive performance when compared with existing classic full-reference (FR) and reduced-reference (RR) point cloud quality assessment methods with a fraction of computational cost.

Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of publicly available underwater image datasets with human subjective scores and reliable objective UIQA metrics. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference (NR) UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e., bluish scene, blue-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions are generated by employing 15 state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores with 52 observers for each image of UID2021 are also obtained by using the pairwise comparison sorting method. Both in-air and underwater-specific NR IQA algorithms are tested on our constructed dataset to fairly compare their performance and analyze their strengths and weaknesses. Our proposed UID2021 dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves the way for further research on UIQA. The dataset is available at https://github.com/Hou-Guojia/UID2021 .

Craniofacial reconstruction is fundamental in resolving forensic cases. It is rather challenging due to the complex topology of the craniofacial model and the ambiguous relationship between a skull and the corresponding face. In this paper, we propose a novel approach for 3D craniofacial reconstruction by utilizing Conditional Generative Adversarial Networks (CGAN) based on craniofacial depth map. More specifically, we treat craniofacial reconstruction as a mapping problem from skull to face. We represent 3D cran- iofacial shapes with depth maps, which include most craniofacial features for identification purposes and are easy to generate and apply to neural networks. We designed an end-to-end neural networks model based on CGAN then trained the model with paired craniofacial data to automatically learn the complex nonlinear relationship between skull and face. By introducing body mass index classes(BMIC) into CGAN, we can realize objective reconstruction of 3D facial geometry according to its skull, which is a complicated 3D shape generation task with different topologies. Through comparative experiments, our method shows accuracy and verisimilitude in craniofacial reconstruction results.

In recent years, image aesthetic quality assessment has attracted considerable attention due to the massive growth of digital images in social platforms and the Internet. However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by various factors, and the criteria for judging the aesthetic of images with diverse semantic information are different. To this end, a Semantic-Aware Multi-task convolution neural network (SAM-CNN) for evaluating image aesthetic quality is proposed in this paper. The network can fuse intermediate features of different layers at different scales in CNN to obtain a more comprehensive and accurate aesthetic expression, under the joint supervision of image aesthetic quality assessment task and semantic classification task in a multi-task learning manner. Besides, by applying the attention mechanism, semantic information with a large receptive field extracted from deep layers is utilised to guide the network to focus on the key parts of features to be fused, to improve the effectiveness of feature fusion. Experimental results on the AVA dataset and Photo.net dataset demonstrate the effectiveness and superiority of the proposed SAM-CNN.

Underwater captured images are usually degraded by low contrast, hazy, and blurry due to absorbing and scattering, which limits their analyses and applications. To address these problems, a red channel prior guided variational framework is proposed based on the complete underwater image formation model (UIFM). Unlike most of the existing methods that only consider the direct transmission and backscattering components, we additionally include forward scattering component into the UIFM. In the proposed variational framework, we successfully incorporate the normalized total variation item and sparse prior knowledge of blur kernel together. In addition, we perform the estimation of blur kernel by varying image resolution in a coarse-to-fine manner to avoid local minima. Moreover, for solving the generated non-smooth optimization problem, we employ the alternating direction method of multipliers (ADMM) to accelerate the whole progress. Experimental results demonstrate that the proposed method has a good performance on dehazing and deblurring. Extensive qualitative and quantitative comparisons further validate its superiority against the other state-of-the-art algorithms. The code is available online at:https://github.com/Hou-Guojia/UNTV

In contemporary society full of stereoscopic images, how to assess visual quality of 3D images has attracted an increasing attention in field of Stereoscopic Image Quality Assessment (SIQA). Compared with 2D-IQA, SIQA is more challenging because some complicated features of Human Visual System (HVS), such as binocular interaction and binocular fusion, must be considered. In this paper, considering both binocular interaction and fusion mechanisms of the HVS, a hierarchical no-reference stereoscopic image quality assessment network (StereoIF-Net) is proposed to simulate the whole quality perception of 3D visual signals in human cortex, including two key modules: BIM and BFM. In particular, Binocular Interaction Modules (BIMs) are constructed to simulate binocular interaction in V2-V5 visual cortex regions, in which a novel cross convolution is designed to explore the interaction details in each region. In the BIMs, different output channel numbers are designed to imitate various receptive fields in V2-V5. Furthermore, a Binocular Fusion Module (BFM) with automatic learned weights is proposed to model binocular fusion of the HVS in higher cortex layers. The verification experiments are conducted on the LIVE 3D, IVC and Waterloo-IVC SIQA databases and three indices including PLCC, SROCC and RMSE are employed to evaluate the assessment consistency between StereoIF-Net and the HVS. The proposed StereoIF-Net achieves almost the best results compared with advanced SIQA methods. Specifically, the metric values on LIVE 3D, IVC and WIVC-I are the best, and are the second-best on the WIVC-II.

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.

Abstract A software framework taking advantage of parallel processing capabilities of CPUs and GPUs is designed for the real‐time interactive cutting simulation of deformable objects. Deformable objects are modelled as voxels connected by links. The voxels are embedded in an octree mesh used for deformation. Cutting is performed by disconnecting links swept by the cutting tool and then adaptively refining octree elements near the cutting tool trajectory. A surface mesh used for visual display is reconstructed from disconnected links using the dual contour method. Spatial hashing of the octree mesh and topology‐aware interpolation of distance field are used for collision. Our framework uses a novel GPU implementation for inter‐object collision and object self collision, while tool‐object collision, cutting and deformation are assigned to CPU, using multiple threads whenever possible. A novel method that splits cutting operations into four independent tasks running in parallel is designed. Our framework also performs data transfers between CPU and GPU simultaneously with other tasks to reduce their impact on performances. Simulation tests show that when compared to three‐threaded CPU implementations, our GPU accelerated collision is 53–160% faster; and the overall simulation frame rate is 47–98% faster.

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.

In this study, a novel underwater colour image enhancement approach based on hue preserving is presented by combining hue–saturation–intensity (HSI) and HS–value (HSV) colour models. In this study, the proposed wavelet‐domain filtering (WDF) and constrained histogram stretching (CHS) algorithms are operated on HSI and HSV colour models, respectively. The degraded image is first converted from red–green–blue colour model into the HSI colour model, wherein the hue component H is preserved and WDF algorithm is executed on the S and I components. Similarly, the image is further converted into the HSV colour model, wherein H component is kept invariant as well and CHS algorithm is applied on the S and V components. The authors' key contribution is that the H preserving method can improve image quality in terms of contrast, colour rendition, non‐uniform illumination, and denoising. In addition, experimental results show that the proposed approach outperforms several other state‐of‐the‐art algorithms.

This paper advocates a novel learning solution to the modeling of long-term spatial-temporal saliency consistency in order to boost the accuracy for video saliency detection. Conventional methods typically utilize the “slack” spatial-temporal model to locally ensure the smoothness of the computed video saliency, yet they could easily encounter the performance tradeoff dilemma (i.e., detection' accuracy and integrity). In contrast, our novel approach proposes the bilevel learning strategy to globally exploit the saliency consistency while overcoming the aforementioned difficulty. Our method first starts with the contrast computation of low-level saliency clues in a frame-wise manner. Then, based on such obtained saliency clues, we devise a novel bilevel Markov Random Field (bMRF) solution to conduct semantic labelling, which can explicitly indicates both the salient salient foregrounds and nonsalient nearby surroundings with high confidence while shrinking the low confidence remains. In such a way, the spatial-temporal consistency constraint is embedded intrinsically into the above explicit semantic labels, and we prevent the performance tradeoff problem from occurring. Next, based on those semantic labels made by our bMRF method, we further propose learning multiple nonlinear feature transformations to enlarge the feature margin between the salient foregrounds and the non-salient nearby surroundings, whose key rationale is to resort to long-term common consistencies to enforce the spatial-temporal smoothness. Thus, we can utilize these learned non-linear feature transformations to simultaneously suppress those short-term false-alarms and correct those hollow effects. To validate our new approach, we conduct extensive experiments on five publicly available benchmarks, and make comprehensive, quantitative evaluations between our method and 17 state-of-the-art techniques. All of the results demonstrate our method's advantages in terms of accuracy, reliability, robustness, and versatility.

A novel bitrate model with low complexity is proposed for perceived compression distortion assessment of mobile video with low resolution, which is extremely useful in intermediate network nodes for quality monitoring. Without fully decoding, parameters are extracted by bitstream analysing, such as bitrate, frame type, quantisation parameter, DCT coefficient, motion vector. Bitrate is regarded as an essential parameter meanwhile the bitrate–MOS curve is determined by video content. Respectively, spatial factor is estimated using quantisation parameter and DCT coefficient and temporal factor is estimated using motion vector. Apart from bitrate, the spatial and temporal factors, which reflect the characteristic of video content, are considered in the proposed model to obtain a more accurate evaluation. Experimental results show that the overall performance of proposed model significantly outperforms that of the other five bitrate models in terms of widely used performance criteria, including the Pearson correlation coefficient (PCC), the Spearman rank‐order correlation coefficient (SROCC), the root‐mean‐squared error (RMSE) and the outlier ratio (OR).

Frame difference method is a good method for motion segmentation, but its result contains much wrong motion regions and incomplete motion objects. In this paper we combine variational method with frame difference method to propose two motion segmentation models, and the proposed models are based on different invariance assumptions. The models can detect motion objects and make up for the inadequacy of frame differential method with smooth terms. Experimental results show that the proposed models can detect motion objects better.

It is common that textures occur in real-word color image, moreover, textures could cause difficulties in image segmentation. For the purpose of solving those difficulties, we put forward a new model. In this model we only need the structural and oscillating components’ information of the real color image. This model is based on the VO model, MTV and active contour models. We will use the fast Split Bregman algorithm to solve this model. The results of our model is mentioned in numerical experiments.

A second order Mumford-Shah model is proposed for image denoising. Unlike the original Mumford-Shah model, the proposed new model uses second order derivatives defined in bounded Hessian space as its regulariser. This model is capable of eliminating the undesirable staircase effect associated with the original Mumford-Shah model with a total variation regulariser. Unlike other second order models that use bounded Hessian regulariser, the proposed new model does not blur the edges in the restored image. To improve computational efficiency, the implementation of the proposed model does not directly solve the high order nonlinear partial differential equations and instead exploit the efficient split Bregman algorithm, which uses the fast Fourier transform. Numerical experiments are conducted to compare the performance of the new model in image denoising with those of the original Mumford-Shah model and the pure second order model.

A mobile ad hoc network (MANET) is a self-configuring network of mobile nodes connected by wireless links without fixed infrastructure, which is originally designed for a cooperative environment. However, MANETs are subjected to a variety of attacks by malicious nodes, in particular for attacks on the packet routing. Compared with traditional cryptosystem based security mechanisms, trust-enhanced routing protocol could provide a better quality of service. In this study, we abstract a basic decentralized effective trust inference model based on node's behavior assessment, where each peer assigns a trust value for a set of peers of interest. In this model, we introduce the 'voting' mechanism to access the recommending experience (or ratings), in order to reduce the cost of the algorithm design and the system overhead. Then combined with this trust model, a novel trust-enhanced multicast routing protocol (TeMR) is proposed. This new protocol introduces the group-shared tree strategy, which establishes more efficient multicast routes since it uses 'trust' factor to improve the efficiency and robustness of the forwarding tree. Moreover, it provides a flexible and feasible approach in routing decision making with trust constraint and malicious node detection. Experiments have been conducted to evaluate the effectiveness of the proposed protocol.

OTSU method is considered to be the best algorithm for image segmentation of threshold selection. It's very simple and it's regardless of image brightness and contrast effects. Therefore it has been widely used in digital image processing. However, in the actual image, because of the influence of noise etc., traditional OTSU algorithm cannot be obtained an accurate segmentation results. In this paper, we divide the CT images of the liver by the combination of the PM filter and local gray stretch. Then we get binary images of vascular. Finally we reconstruct the venous system in liver by the use of Visualization Toolkit (VTK) and 2D segmentation results.

The variational level set model for piecewise constant multiphase image segmentation on the plane and the related Split-Augmented-Lagrangian Projection Method (SALPM) are investigated in this paper. On the analysis of the current problems based on the variational level set method for image segmentation, we also design a rapid SALPM method for two-phase image segmentation model, getting the general model of multiple phase level set in order to facilitate the generic design and program. In addition, the concrete formula of the rapid split algorithms for multiphase image segmentation with level set model and the calculation steps are given, and simultaneously taking liver tumor CT image as examples of the multiphase segmentation. Moreover, by comparing with the traditional methods, the experiments show that our algorithm presented in this paper have higher computational efficiency and accuracy, and better the extraction of liver contour.

Motion deblur is a very hot and hard research topic currently because it is an ill-posed problem. In this paper, we proposed using normalized nonlinear diffusion regularization for motion deblurring. To reduce the complexity of solving the deblurring equation, a fast method called Split method is used. Because the result derived from the energy function give the lowest cost, our method doesn't need any auxiliary method for solving the energy function besides multiscale implementation. Using the estimated kernel, the final clear image can be got by total variation method. Experiments demonstrate the validity of the proposed method.

Independent Component Analysis (ICA) has found its application in face recognition successfully. The goals are to estimate the components from raw image data. These components are then used to extract features of face images on which face classification is conducted. The components play key role in face recognition system. However these separated components are not equally important in terms of contribution to the feature extraction. ICA components are un-ordered. We do not know which component is more valuable than others. In order to improve ICA performance it is highly desired to select most discriminative components that are most effective. It is of great significance for ICA face recognition to find methods for optimizing independent components (ICs). In this paper we explored two methods for this purpose. One is ICA Component Subspace Optimization, the other is Sequential Forward Floating Selection (SFFS).

The general variation diffusion models for additive and multiplicative noise removal of color images are proposed and their Split Bregman algorithms are designed via introducing auxiliary variables and Bregman iterative parameters, which lead to simple Poisson equations and analytical soft threshold formulas of the original minimization problems. The MTV (Multichannel Total Variation) and MPM (Multichannel Perona Malik) regularizations are considered as two examples of the proposed general regularizer and used for additive and multiplicative noise removal of color images with different kinds of noise. Finally, some numerical experiments are provided to validate the models and algorithms proposed in this paper.

The user and services provided by resources in grid environments are dynamic. Therefore, the malicious nodes may exist in grid system. It will significantly affect the quality of service (QOS) requirements of users, even lead to the economic loss of user in the grid economic environment. So a certain resource management scheme has to be implemented in grid economy environments to ensure the environments working properly and achieve better scalability. It is recognized an effective measure which uses Reputation to control the access of malicious nodes. In this paper, we design a distributed reputation control architecture which is based on virtual organizational domains (VOD) and propose a reputation calculation algorithm which spans these domains. The inner structure and this framework are also detailed discussed here. At last, through the simulating experiments, it can be proved that this architecture could block the assessment of malicious nodes effectively and improve the efficient and stability of nodes in grid economy environments.