The imaging process of the underwater image is similar to the haze image. However, the dehazing methods fail when used in the underwater image restoration because of the color attenuation and blue (green) color tone, caused by the selective absorption of water and light scattering. Thus, this paper proposes a new approach for underwater images restoration based on the idea of removing backscattering after the color cast removal. Due to the attenuation of light in water, a color cast removal approach is proposed. The relationship between scattering coefficient and wavelength is used to obtain a more accurate transmission estimation for each color channel. In addition, an improved algorithm for background light estimation is presented, which can effectively avoid the influence of artificial light, white object and noise. Experimental results demonstrate the effectiveness of the proposed method in restoring the original color of the scene and removing the backscattering.

The depth information in volume data is lost in the image rendered by volume rendering technique. The existing methods of depth perception enhancement only enhance some structures in the volume data at the cost of other structures details, and they directly edit the volume rendering algorithm. For ray-casting algorithm, a method of depth perception enhancement is presented, and it does not directly edit the algorithm. Specifically, an inerratic grid is projected to the surface of volume data, and then the grid changing along surface is rendered in the final image. Users can apperceive the depth information of surface from the changed grid. Meanwhil, two methods are used to enhance the depth information of the grid projection lines, one is coloring the grid lines based on the depth, and the other one is adding accessorial lines to join the grid lines on two surfaces with different depths. When implemented using compute unified device architecture, the image is rendered in real-time under user interaction. The effect of depth perception enhancement in the final image is obvious especially when the volume data contains some disjunct or intersectant objects.

To overcome the shortage that the spatial connectivity of every node is modeled only via the k-regular graph and the idealistic prior background assumption is used in existing salient object detection method based on graph-based manifold ranking, an improved method is proposed to increase the precision while preserving the high recall. When constructing the graph model, the affinity propagation clustering is utilized to aggregate the superpixels (nodes) to different color clusters adaptively. Then, based on the traditional k-regular graph, the nodes belonging to the same cluster and located in the same spatial connected region are connected with edges. According to the boundary connectivity, the superpixels along the image boundaries are assigned with different background weights. Then, the real background seeds are selected by graph cuts method. Finally, the classical manifold ranking method is employed to compute saliency. The experimental comparison results of 4 quantitative evaluation indicators between the proposed and 7 state-of-the-art methods on MSRA-1000 and complex SOD datasets demonstrate the effectiveness and superiority of the proposed improved method.

In the process of the objective research in traditional Chinese medicine tongue diagnosis, the tongue information obtained from the analysis of 2D tongue image is limited. It restricts the development of tongue diagnosis. Based on the feasibility of photometric stereo method to reconstruct the static tongue mode, for reconstructing the 3D surface of the real tongue, a dynamic information collection system is designed for image information acquisition. Then the normal vectors, texture reflectance and depth values are obtained with photometric stereo method, and the 3D surface of tongue is displayed based on DirectX. Finally, a highlight excluding algorithm is used to reduce the effects of the highlight areas on the reconstruction results. After the highlight excluding, it performs better in the reconstruction accuracy and robustness aspects. Experimental results indicate that the average relative error of this algorithm is of 7.24%. The results can express the tongue surface morphology and details such as tooth marks visually.

Existing visual tracking methods based on incremental Principal Component Analysis (PCA) learning have two problems. First, the measurement model does not consider the continuation characteristics of the object appearance changes. Second, when the manifold distribution of target appearance is non-linear structure, the incremental principal component analysis learning based on fixed update frequency can not adapt to changes of subspace model. Therefore, the more reasonable a priori probability distribution of targets is proposed based on the continuity of the object appearance changes in the subspace model. Then, according to the matching degree between the current tracking results and the subspace model, the proposed method adaptively adjusts forgetting factor, in order to make the subspace model more adaptable to the object appearance change. Experimental results show that the proposed method can improve the tracking accuracy and robustness.

In order to deal with the color overlap problem in matting, a fast random projection method is proposed to complement the color information. First, the raw texture matrix is obtained through dense abstraction from color image. The random projection is performed and the best three texture channels are chosen by the foreground and background overlap factors. Combining the texture image, the new cost function takes into account texture, color, and spatial information. Second, the filtering process is carried out to the sample selection cost, including the effect of the local and nonlocal neighbors. Finally, the relationship between iterative filter and global energy smooth is proven, and the post filter formula is obtained. Experiments show that the cost filtered matting with random texture features produces both visually and quantitatively better results when the color distributions of the foreground and background are similar.

Image Quality Assessment (IQA) is widely used in digital image processing, and No Reference IQA (NR-IQA) has become the research focus recently. This paper proposes an NR-IQA method based on local structure, which chooses strong structure areas by using local gradients, and assesses the quality of image by utilizing the Maximum Local Gradients (MLG) of strong structure areas. The main novelties are: pixel,s quality assessment based on MLG; whole image quality based on strong edge points, quality. The proposed method can assess noise image and blur image at the same time, and the score of the proposed method is smaller when the distortion is more serious. The results show that the proposed no-reference method for the quality prediction of noise and blur images has a comparable performance to the leading metrics available in literature.

To improve the image edge detection accuracy and anti-noise performance, a new approach for image edge detection based on conformal phase is proposed. Firstly, the proposed approach can effectively improve the precision of edge detection and restrain the false edge and noise by using respectively the conformal monogenic signal which could express local structure of the image with different intrinsic dimensions and an exponential function to calculate the phase deviation. Secondly, it can reduce the complexity of the algorithm by taking advantage of the Poisson kernel of existence of analytic representation in spatial domain. To demonstrate the advantages, the proposed approach is compared with the existing methods?of phase congruency based edge?detection. The simulation experiment results show that the proposed approach can extract image edge more accurately, more completely, and more uniformly, with better robustness to noise and lower computational complexity.

In order to better solve the low-rank and sparse decomposition problem for high-dimensional data matrix, this paper puts forward a novel Max minimization model with Max-norm as the convex relaxation of the rank function, and provides the corresponding algorithm. Based on the complexity analysis on the novel model, an improved Max constraint model is further proposed, which not only has good performance in the decomposition problem but also can be solved with a fast projection gradient method. The experimental results show that the proposed two models are effective for low-rank sparse decomposition problem.

The Total Variation (TV) method is often used to reconstruct the Compressed Sensing Magnetic Resonance Imaging (CS-MRI), however, it can generate the "stair effect” in the reconstructed MR image. In this paper, there types of TV extension based methods, i.e. High Degree Total Variation (HDTV), Total Generalize Variation (TGV) and Group-Sparsity Total Variation (GSTV), are proposed to implement the sparse reconstruction of MR image. In addition, the shift-invariant discrete wavelet transform are integrated into these TV extension based methods as the sparsifying transform. The Fast Composite Splitting Algorithm (FCSA) is adopted to solve the convex optimization problem of CS-MRI reconstruction. And the Two different types of MR images with radial sampling trajectory are used to validate the reconstruction performance of CS-MRI by using the TV extension methods. The experiment results show that the TV extension based models can overcome the shortcomings of TV based model. Moreover, compared with HDTV and TGV methods, the GSTV method can obviously improve the reconstruction quality with higher Signal-to-Noise Ratio (SNR).

An operator-based approach for adaptive signal separation is proposed by using the locally orthogonal constraint and adopting back projection strategy. The approach adaptively separates a signal into additive subcomponents and a residual signal, where the subcomponents are in the null space of the operators. Experiments, including simulated signals and a real-life signal, demonstrate the feasibility, efficiency, and practicability of the proposed approach for solving the mode mixing phenomenon.

A novel and efficient algorithm is proposed for Space-Time Block Code (STBC) classification, when a single antenna is employed at the receiver. The algorithm exploits the discriminating features provided by the forth-order cumulants of the received signals. Higher-order cumulants (of order greater than 2) are used to eliminate the impact of noise. Firstly, the theoretical value of the different STBCs is caculated, then the samples of STBCs are classified with an interval detector. It does not require estimation of the channel information and signal-to-noise ratio of the transmitted signal. Simulation results show that the proposed method for blind recognition of STBC achieves good performance.

This paper presents an efficient algorithm to design high-complexity Discrete Fourier Transform (DFT) modulated filter bank with double-prototype. The algorithm is based on unconstrained optimization, where the design problem is formulated into an unconstrained optimization problem, whose objective function is the weighted sum of the transfer distortion, the aliasing distortion of the filter bank, and the stopband energy of the Prototype Filters (PFs). The optimization problem can be efficiently solved by utilizing the bi-iterative scheme. The matrix inverse identity and the fast algorithm for Toeplitz matrix inversion are employed to dramatically reduce the computational cost of the iterative procedure. Numerical examples and compared tests to show that compared with the existing methods, the proposed method possesses much lower computational cost and can be used to design large-scale filter bank with better overall performance.

Research on clustering heterogeneous information networks is one of the current hotspots. Taking advantages of the sparsity of heterogeneous information networks, a fast clustering algorithm based on embedding technology for heterogeneous information networks of star network schema is proposed in this paper. First, the heterogeneous information network is transformed into some compatible bipartite graphs from the point of compatible view. Then, the approximate commute distance embedding of each bipartite graph is computed via random mapping and a linear time solver, and an indicator subset in each embedding indicates the target dataset. At last, a general model is formulated via all the indicator subsets, and a minimum value of the model is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. This proposed algorithm is effective by theory analysis and experimental verification.

End hopping technology is one of the hot research domains in the field of proactive network defense. An end hopping model based on fixed time slot under the fixed policy is established. The defense gains decline caused by fixed hopping period and the service loss caused by data packet loss on hopping boundary are analyzed. The real-time network anomaly assessment algorithm based on the fusion of nonextensive entropy and Sibson entropy is proposed. Then, the selfadaptive end hopping period and space policy based on the proposed algorithm are designed and the proactive network defense model is constructed which improves the defense gains. Furthermore, Hopping period stretching policy based on network delay prediction is proposed to ensure the service quality on hopping boundary. Theoretical analysis and simulation results show the effectiveness and good service of the proposed model in network defense.

To solve the complexity of Routing and Wavelength Assignment (RWA) in distributed satellite optical network, the Ant Colony Optimization (ACO) based on Small Window Strategy (SWS) is put forward. The link duration and the wavelength idle ratio are used as the heuristic functions for load balancing and decreasing the blocking probability. The small window strategy is introduced to limit the routing in the Minimum Routing Request Range (MRRR) and promote the convergence speed. By calculating the intersection of idle wavelengths on the adjacent links, the algorithm can accomplish the routing selection and wavelength assignment by a single ant. The properties of the algorithm in both single and double master satellites cases are analyzed, and the results show that compared with Dijkstra+FF algorithm, the blocking probability of ACO can reduce at most 0.5 and 0.7 for single and double master satellites respectively, and the improvement of resource utilization ratio can reach to 0.45 and 0.50.

Distributed antenna based full-duplex relay system is capable of simultaneous transmission and reception in the same frequency band on two hops, and it provides uniform coverage for cell edge and deep shadow fading areas with increased spectral efficiency. In multiuser scenarios with non-ideal self interference cancellation, beamforming using multiple distributed antennas is proposed to suppress self interference and multiuser interference jointly. A system model for multiuser end-to-end sum-rate maximization under individual power constraints at distributed antennas is established first. Then, a dual-layer iterative algorithm is proposed to resolve the non-convexity of the problem. Simulation results validate the effectiveness of the proposal algorithm, showing that the proposed beamforming design can be used in distributed-antenna based full-duplex relay systems, to suppress both self interference and multiuser interference efficiently, and increase system spectral efficiency significantly.

In this paper, a distributed peer-to-peer beamforming technique in frequency-selective relay networks is proposed. It is assumed that all the relay nodes use Filter-and-Forward (FF) protocol to compensate for the source-to-relay and relay-to-destination channels. All the channels of the active source-destination pairs are considered to be frequency-selective. The beamforming strategy that minimizes the total relay transmitted power subject to the Quality-of-Service (QoS) constraints for all of the destination nodes is considered. The resultant problem is approximately solved using Semi-Definite Programming (SDP). Simulation results demonstrate that in frequency-selective multiuser relay networks, the proposed technique substantially outperforms the existing amplify-and-forward peer-to-peer beamforming methods.

To solve the issues of the high complexity, poor performance, and slow convergence speed in the blind equalization of high order Continuous Phase Modulation (CPM) signals, a new blind equalization method based on Forward Adaptive Backward Adaptive Soft-Input Soft-Output (FABA-SISO) algorithm used in linear modulation signals is developed from the perspective of bidirectional adaptive channel equalization. A novel adaptive blind equalization algorithm for high order CPM signals is proposed based on the combination of Per-Survivor Processing (PSP) and Kalman filtering. The algorithm improves the equalization performance by applying the FABA-SISO which uses the past, the present and the future observation to implement Kalman filtering channel estimation. Simultaneously, a PSP algorithm is used for further improvement of the system complexity, so that the algorithm is better suitable for engineering application. The simulation results show that the proposed algorithm provides a good blind equalization performance and convergence.

To solve the problems of security threats and energy constrained in wireless networks, this paper studies secure communication of energy harvesting Gaussian wiretap channel based on Save-then-Transmit (ST) protocol. Firstly, the optimization of the system secrecy rate is studied. Next, to further improve the system secrecy rate, a Cooperative Jamming (CJ) scheme is given. Besides, the sufficient and necessary conditions for this scheme to achieve a higher secrecy rate are discussed. Then, an iterative optimization algorithm of the secrecy rate in this scheme is proposed. Finally, a low complexity selection scheme for single helper is given. Simulation results show that, the first optimization scheme obviously improves the system secrecy rate. The second cooperative jamming scheme can further enhance the system secrecy rate and has fast convergence rate. When the original energy harvesting Gaussian wiretap channel can not operate secure communication, the cooperative jamming scheme can achieve secure transmission under certain conditions.

Because of the poor effect of the traditional coding methods on the screen content coding, considering the screen content is rich in non-continuous tone content, a new intra coding mode based on High Efficiency Video Coding (HEVC), which is called Intra String Copy (ISC), is proposed. The basic idea is adopting the dictionary coding tool on the HEVC Coding Unit (CU) level. When encoding, the current CU pixels are searched and matched in a certain length dictionary window by using Hash table. When decoding, according to the pixels string matching distances and lengths, the current CU pixels in the reconstruction cache are restored by copying the corresponding position pixels. Experiment results show that with little coding complexity increase than HEVC, for typical screen content test sequences, ISC can achieve lossy coding bit-rate saving of 15.1%, 12.0%, 8.3% for All Intra (AI), Random Access (RA), and Low-delay B (LB) configurations, respectively, and lossless coding bit-rate saving of 23.3%, 14.9%, 11.6% for AI, RA, and LB configurations.

Rotation-symmetric Boolean function is a class of Boolean functions with good cryptographic properties, and researches on its weight and nonlinearity cryptographic properties have good theoretical value. Different from the conventional calculation method, in this paper, these problems are converted to the evaluation of exponential sum on finite fields with a specific normal basis. Some new results about the weight and nonlinearity of some rotation-symmetric Boolean functions of degree 2 with  4 ?? n and n=2^s  are obtained. Using the proposed method, the weight and nonlinearity of almost all Rotation-symmetric Boolean functions of degree 2 can be evaluated. This new method is also interesting for studies on the other Boolean functions.

A novel and low complexity algorithm is proposed to estimate the parameters of air maneuvering target based on Compressive Sensing (CS) and Cubic Phase Transform (CPT). First of all, CPT is utilized to separate the two parameters of the maneuvering target. Then, CS is used to estimate the parameters according to the properties of sparse signal in the time-frequency domain. The proposed algorithm can acquire precise parameter estimation with limited pulses in a coherent processing interval for airborne radar. The effectiveness of the proposed algorithm is verified by the numerical simulations.

Estimation of topography for the generation of Digital Elevation Models (DEM) requires the absolute interferometric phase. However, the existing absolute phase determination methods are complicated for processing the Ultra-WideBand (UWB) Synthetic Aperture Radar Interferometry (InSAR) data. To resolve this problem, a new approach is proposed in this paper. First, to acquire the high accuracy image registration result, the registration offsets are obtained from the interpolation of the offsets of the control points. Then, based on the offsets, the interferometric phase is computed and divided into two partsthe Registration Phase (RP) and the MisRegistration Phase (MRP). The RP is derived from the registration offsets, and the MRP is dependent on the unknown misregistration. Theoretical derivations show that the MRPs are unambiguous in most high coherence areas, so MRP can be unwrapped efficiently, and its absolute phase can be obtained directly without using any auxiliary data. Finally, the absolute interferometric phase is obtained from adding the RP and the true MRP. Compared with the existing algorithms, the proposed approach has lower complexity. Experimental results on P-band UWB InSAR data prove its effectiveness.

The Range Migration Correction (RMC) is a key technique of synthetic aperture radar altimeter which is more precise than the conventional radar altimeter. Because of the satellite motion, the distance change between the satellite and the observed target will bring about some residual errors, but they are ignored in the existing RMC algorithms. In this paper, the influences of the vertical and horizontal velocities of the satellite are studied, then an RMC model is builded, and finally a new RMC algorithm which corrects not only the slant range error but also the residual errors is proposed. The simulation results show that this new algorithm can obtain more accurate outcomes.

For Inverse Synthetic Aperture Radar (ISAR) autofocus imaging, this paper proposes a high-resolution imaging method based on Bayesian Compressed Sensing (BCS). Firstly, according to the sparsity characteristics of target image, a sparse model with the hierarchical framework is established, which can achieve better approximation to the original l0 norm. Then, the phase errors are assumed to obey the uniform distribution. Next, following the criterion of Maximum A Posteriori (MAP), target image and phase errors are solved using alternate iteration based on BCS theory. Compared with traditional methods, the proposed method further combines the joint sparse information of target image, and converts the ISAR CS imaging into solving a joint Multiple Measurement Vector (MMV) sparse optimization problem, which can improve both the autofocus precision and the imaging quality efficiently. Simulation results show the effectiveness of the proposed method.

Repeater deception jamming against Linear Frequency Modulated (LFM) pulse-compression radar is realized by frequency-shift repeater and direct repeater jamming so far. Conventional repeater jamming type is simple. Regularity of jamming signal is strong and complexity is low. A new repeater jamming type with multi-carrier modulation based on intermittent sampling is proposed. Firstly, the model of intermittent sampling is rebuilt with the code chip concept. Based on this, lifelike false targets with the quantity, amplitude and space distribution which can be controlled are produced by attaching different frequency-shift component to the present sampling code chip, deserializing signal used multi-carrier parallel modulation system and utilizing the accumulation of different times repeater signal jamming effect among sub-carriers. The simulation results show that the new jamming type has better performance than frequency-shift jamming and direct repeater jamming.

A space coning target has the typical micro-motion. A novel parameters estimation method for space coning target based on two-aspect range profile sequences is proposed in this paper. The parameters of space coning target include precession parameters and structure parameters. First, this paper analyzes the trace of the radar elevation angle when the target is in free phase. Using the established precession model, the equation for the projections of the target’s scatters onto the Radar Line Of Sight (RLOS) is derived. Then, analytical solutions of the parameters are obtained based on the two-aspect range profile sequences. Ballistic curve is introduced to solve the problem that the estimation of half cone angle requires high Signal-to-Noise Rate (SNR). Finally, the experiments verify the effectiveness of the proposed method by using electromagnetic data.

The interference fringe phenomenon appears when continuous wave subsurface penetrating radar is used to image on the uneven surfaces for nondestructive detection. As one of the main disturbances, the fringes will deteriorate the imaging results. The principle of this phenomenon is briefly studied and a filtering method to remove the fringes based on the distribution difference in frequency domain between cylindrical surfaces and target is proposed. Besides, according to the regular angle distribution of small target in frequency domain, a compensatory method by interpolation in a certain angle is studied to optimize the imaging results. Moreover, an effective imaging process for cylindrical subsurface detection based on the wavefront imaging algorithm is illustrated. The numerical and experimental data validate the applicability of proposed method and the results outperform the traditional approach of average subtraction.

Bistatic radar has an advantage in the anti-stealth and low altitude defense, but the bistatic scattering data measured from the terrian surface are extremely scarce. To solve this problem, the genetic algorithms and the backscattering data from the soil, concrete and the sand surface in L/S/X/Ku band are used to retrieve the effective permittivity and the roughness parameters of the land, and then the bistatic scattering data are predicted. The research above proves that the land equivalent surface scattering model is effective. The bistatic scattering echo increases with frequency, and it first increases and then decreases along with the scattering angles, first decreases and then increases along with the scattering azimuth angles. The minimum value of the bistatic scattering echo always appears in the 90 degree azimuth angles for the HH polarization, and it shifts from 90 degree azimuth angles to the small angle direction for the VV polarization. And also it is related to incident frequency, the moisture and the roughness of land. The bistatic scattering characteristics of land surface can be used for the anti-stealth research and the inversion of the land parameters.

In the cooperative navigation algorithm for multiple Autonomous Underwater Vehicles (AUVs) with a single leader, the model of the system is nonlinear. The Extended Kalman Filter (EKF), which is directed against the nonlinear system, is one of the most influential techniques. However, the performance of EKF critically depends on a large number of modeling parameters which can be very difficult to obtain, and are often set by manual tweaking and at a great cost. In this paper, a method for automatically learning the noise covariance of a Kalman filter is applied, and the simulation result shows that this algorithm fully automatically and quickly outputs the noise covariance, which improves the navigation accuracy of the cooperative navigation system.

The Time Difference Of Arrival (TDOA) histogram is effective for pulse train de-interleaving in radar detection. The performance of TDOA-histogram based pulse sorting algorithm depends on several parameters in the histogram, such as the Pulse Repetition Interval (PRI) detection threshold and the box length, which are set posteriorly in traditional TDOA-histogram based algorithm. In this paper, the explicit expressions of detection threshold for various PRI modes (i.e. stable, jitter, and stagger) are derived, and the relationship among these parameters are revealed. Consequently, a signal sorting algorithm is proposed, and its performance is validated by simulation in complex signal environment.

To investigate the remote detecting approaches of weak transient electromagnetic signals, the detecting method based on the general cross correlation and chaotic time series prediction integrate algorithm is proposed. Based on the double antennas test and cross correlation information estimation, the signal detection of weak non-periodic discharge signal in low Signal to Noise Ratio (SNR) is transformed to the estimation of periodic time- delay parameters, which decreases the level of noise simultaneously. The results of estimation are predicted based on chaotic predicting method, and the mean value of predicting error is the detection results of target signal. The feasibility of the approach is analyzed by simulating and experimental method. The results show that in the low SNR, the integration method can effectively restrain the interference from noise. Compared with the traditional cross correlation method or chaotic prediction algorithm, the detecting probability is higher for weak transient electromagnetic signals. Furthermore, the pulse integrating algorithm needs the less accumulation times, and its detecting efficiency increases. Hence, the proposed integrate algorithm is suitable for remote detection of partial discharge source.

Orthogonal Frequency Division Multiplexing (OFDM) radar is a newly developed radar system, which uses the OFDM principle in communication systems, and has some unique advantages over traditional radar systems. This paper formulates the characteristics of OFDM radar, reviews thoroughly the Literatures on OFDM new radar system, summarizes the research achievements in the key research directions of signal properties and waveform design, signal processing, and new radar system, and finally analyzes the future development tendencies and application prospects of OFDM radar.

Content-Centric Networking (CCN) is a new Internet architecture with native support for scalable and efficient content acquisition, which is proposed to accommodate the changes in future communication mode. Content caching is one of the key issues in CCN. In some existing work, the choice of caching nodes is over-focused on few special nodes, which results in an uneven distribution of cached contents. It greatly decreases the utilization of network resources and impairs the overall caching performance. In this paper, a Cooperative Caching Mechanism with Content Migration (CCMCM) is proposed. In this scheme, the centrality of node is considered in the selection of caching nodes to ensure that contents can be cached in the more important nodes as much as possible. When the cached contents are extensive, the caching node can transfer some contents to the appropriate neighbor according to the cache space available, the cache replacement rate and the connection stability between nodes. The aim is to fully utilize the resource of neighbor nodes and achieve effective load distribution. Simulation results show that the proposed scheme improves the load balance among caching nodes, increases the resource utilization and achieves high cache hit rate with low average access cost.