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To solve direction-finding problems for nested arrays under conditions of impulse noise and gain–phase errors, this paper proposes a direction-of-arrival (DOA) estimation method. First, a correntropy with an adaptive kernel length combined with an infinite norm of a variable weight value is proposed to suppress impulse noise. Then, the auxiliary array elements are used to realize the initial calibration of the amplitude and phase errors. On this basis, a DOA estimation method without a spectral peak search is proposed; this method is robust for arrays that still have residual gain–phase errors after calibration. Our method overcomes the difficulties of DOA estimation for nested arrays under conditions of gain–phase errors and impulse noise. Compared with traditional methods, the proposed method does not require prior knowledge, does not require a spectral peak search and has high superiority and robustness; a series of experiments have verified its superiority. In addition, a Cramér‒Rao bound (CRB) for angle estimation under conditions of gain–phase errors and impulse noise is proposed in this paper. [ABSTRACT FROM AUTHOR]

In Massive multiple-input multiple-output (MIMO) systems, channel estimation is crucial. The large size of the antennas causes a significant pilot and feedback overhead, making it challenging to estimate channels in massive MIMO systems. Besides, the studies have shown that mmWave channels are sparse due to the limited number of dominant propagation paths. Therefore, the motivation of this paper is to exploit the inherent sparsity of the massive mmWave MIMO channels to develop a semi-blind channel estimation with reduced number of pilots. To this goal, an expectation maximization (EM) based technique has been developed which leverages the sparsity of the underlying channel for its better estimation. An iterative approach is proposed to solve the modeled problem which simultaneously updates the channel coefficients and data symbols using available data and system structure at each iteration. The proposed method imposes sparsity with the aid of Smoothed L 0 norm (SL0) in the M-step. The simulation results demonstrate the proposed method have quick convergence and lower channel estimation error compared to the existing methods. As a quantitative evaluation, the proposed method attains the normalized mean square error of 9 × 10 − 2 and 7 × 10 − 3 at SNR = 5 dB and SNR = 15 dB , respectively. [ABSTRACT FROM AUTHOR]

Accurate and fast vehicle detection is a key factor in the field of intelligent transportation. To solve the problems such as low recognition accuracy, slow detection speed, and poor robustness existing in current vehicle detection algorithms. This paper proposes a vehicle detection algorithm based on YOLOv5 and the coordinate attention mechanism (CA) named YOLO-CCS. YOLO-CCS enables the network to focus on the vehicle itself during the feature extraction process, reduces the loss of feature information and improves the effect of vehicle detection. In our algorithm, we firstly expand YOLOv5s with CA blocks at the location of feature extraction section of network backbone and enhance the extraction capabilities of key features, and suppress the interference of complex backgrounds by embedding position information. Secondly, to enable the network to extract rich feature information and improve the feature fusion capability of the network, we introduce the faster implementation of the Cross Stage Partial (CSP) Bottleneck with 2 convolutions (C2f) in the backbone and neck of the network, which has more residual blocks and skip connections, which provides rich feature semantic information, and heightens the feature extraction capabilities of the network. Thirdly, we integrate the SCYLLA-IOU (SIOU) loss function suitable for YOLO-CCS. So we can leverage the vector angle between the real box and the predicted box to further improve the accuracy of the algorithm and accelerate the convergence of the model. Experimental results show that, compared with the baseline model YOLOv5s, the m A P 50 and m A P 50 − 95 of our method increase by 3.2% and 1.7% respectively. Compared with other YOLO based models, m A P 50 has increased by approximately 4%. And the detecting speed of YOLO-CCS algorithm reaches 48 FPS, which can meet the real-time requirements of vehicle detection. [ABSTRACT FROM AUTHOR]

The problem of tensor completion (TC) has significant practical significance and a wide range of application backgrounds. Various approaches have been used to solve this problem, including approximating the rank function through its convex envelope, the tensor nuclear norm. However, because of the gap between the rank function and its convex envelope, this method is often unsatisfactory in terms of achieving recovery. As a result, researchers have widely studied explicit non-convex functions that can better approximate the rank function. In this paper, we construct a novel shrinkage operator that functions as the proximal operator of a non-convex function satisfying three critical properties: unbiasedness, sparsity, and continuity. While an explicit analytical expression for the induced non-convex function cannot be obtained, simulation experiments show that it approximates the rank function. By implementing the shrinkage operator in the TC framework, we can show that our iterative sequence converges to the Karush-Kuhn-Tucker (KKT) point. We have discovered that the convergence of the model can be guaranteed as long as certain properties hold for the shrinkage operator. Hence, this result can be extended to a class of shrinkage operators. Extensive experimental results illustrate that our proposed method achieves better recovery performance at the same sampling rate compared to other methods. • We construct a shrinkage operator that satisfies the three important properties. • This shrinkage operator can induce a non-explicit, non-convex function. • Extending the convergence of the model result to a class of shrinkage operators. [ABSTRACT FROM AUTHOR]

A distributed network with highly colored input signal is often located in a scenario where the output signal is subject to impulse noise. Under the above scenario, the performance of the diffusion adaptive algorithms will suffer from performance degradation, which includes distributed parameter estimation accuracy and convergence/tracking rate. To solve these problems, the diffusion affine projection like maximum correntropy (DAPLMC) algorithm is proposed. Moreover, highly colored input signals tend to be mixed with noise, which will lead to biased estimation. To deal with the adverse impact of biased estimation, based on the bias compensation (BC) strategy, the bias-compensated DAPLMC (BC-DAPLMC) algorithm is proposed in this paper. By analyzing the convergence performance of the BC-DAPLMC algorithm, a range of its step size is obtained, and the steady-state error of the BC-DAPLMC algorithm is studied. The superior performance of the BC-DAPLMC algorithm and theoretical steady-state error analysis is validated by simulation results. [ABSTRACT FROM AUTHOR]

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can produce time-stationary range-angle dependent beampattern. The problem with these beampatterns is the repeating lobes in the angle-range domain. Different methods are proposed to obtain better beampattern in terms of grating lobe and sidelobe suppression by using frequency offsets. However, these methods mostly choose the frequency offsets using an analytic expression and do not offer a control on the resulting range beampattern. In this paper, a new method for transmit beampattern design in range is presented. This method transfers the design problem into a tangential space and solves the problem using complex manifolds through an optimization procedure. The proposed approach considers amplitude tapering in the transmitter antenna elements and the practical instrumented range of the radar. Range beampattern constraints can be defined conveniently leading to an effective design process. Several simulations are done to show that the proposed method can significantly improve the beamwidth and sidelobe level in range patterns. • Transmit beampattern design by finding frequency offsets optimized for range folded clutter and minimum sidelobe level. • Convenient range beampattern constraints defined in terms of pass, stop and transition band. • Practical beampattern design without using data aided computationally expensive methods. • Flexibility in terms of amplitude tapering in the transmitters. [ABSTRACT FROM AUTHOR]

As we all know, conformal arrays can provide smaller radar cross-section (RCS) and greater angle coverage than conventional linear arrays and planar arrays. In this paper, we develop a robust adaptive beamforming (RAB) approach using the cylindrical uniform conformal array. Firstly, we reconstruct the interference-plus-noise covariance matrix (INCM) by estimating the steering vectors (SVs) and powers of all interferences. Specifically, we estimate the interference SVs by exploiting the MUSIC spectrum and corrected by solving a minimization problem based on the orthogonality between the interferences SVs and noise subspace, and the interferences powers are further estimated by utilizing the non-correlation between different signal SVs. With the reconstructed INCM, the SV of the signal-of-interest is estimated by solving a quadratically constrained quadratic programming problem. Numerical results demonstrate that the proposed approach is obviously superior to the existing RAB techniques. [ABSTRACT FROM AUTHOR]

The Hamming coding ( n , k ) + 1 data hiding (H1DH) scheme is based on matrix encoding and has the characteristic of a good embedding efficiency. This scheme improves the embedding efficiency of Hamming coding ( n , k ) data hiding (HDH). H1DH is used to hide a k + 1 bit in a ( n + 1 ) block by adding or subtracting one. However, the embedding rates of HDH, H1DH, and HCIH are only 0.43, 0.5, and 0.75 respectively. The important criterion of data hiding is the amount of embedding capacity with a high PSNR in a cover image. In this paper, we propose a Hamming coding ( n , k ) + k data hiding (HkDH) scheme to hide 2 k bits in a block with overlapped LSB and 2LSB using matrix encoding by adding or subtracting by two. In order to hide 2 k bits in a block, it is necessary to increase or decrease by one, two or three. Assuming that we need two modifications to hide 2 k bits in a block and the location of two modifications is the same, the collision problem occurs in the block. We suggest an optimal method for hiding 2 k bits in a block and verify the proposed method. The main contribution of this paper is theoretically proving that our HkDH scheme has a better embedding capacity than HDH, H1DH, EMD, and HCIH while not seriously degrading the PSNR. [ABSTRACT FROM AUTHOR]

Most Siamese trackers based on graph attention models focus on the topology between nodes when extracting features. These approaches ignore contextual information regarding the overall structure of the target, resulting in a diminished capacity for target discrimination. In this paper, to resolve this issue, we propose a global semantic-guided graph attention network for Siamese tracking. Firstly, in order to balance the node constraints with the overall information of the target, a global semantic awareness module (GSA) is proposed. It utilizes the attention strategy to focus on the overall characteristics of the target, aiming to mine the relationship of the target context. Incorporating it into the semantic-guided graph attention network through cross-correlation operations can avoid the problem of focusing only the information between nodes without being able to effectively characterize the target. Secondly, to enhance the accuracy of single-point feature regression results, a novel extreme point feature aggregation module (EPA) is proposed. We obtain feature information of the extreme points on the bounding box and combine them with single-point features for the bounding box feature representation to improve the regression accuracy. Finally, to establish a connection or bridge between the classification and regression subnetworks, a categorical regression alignment network is designed. An IOU-guided ranking loss is introduced to align the classification confidence with the IOU of the corresponding positive sample localization prediction to solve the problem of misalignment of the classification branch with the regression branch effectively. Experiments conducted on five challenging benchmarks datasets, namely GOT-10K, OTB-100, LaSOT, UAV123, and TC128, demonstrate that our approach exhibits superior performance compared to other tracking methods in terms of tracking accuracy and execution efficiency. [ABSTRACT FROM AUTHOR]

In order to suppress random noise and remove stripe interference in ISAR imaging, a Compressive Sensing method is proposed for super-resolution ISAR imaging in this paper, which is named LR-ASTV (Low-rank and Anisotropic Spatial Total Variation) algorithm here. In this algorithm, the original echo HRRP is transformed into echo HRRP of MMV model with radial interpolation processing at first. Subsequently, an optimization objective function based on Compressive Sensing framework is formulated which includes the sparsity constraint and low-rank constraint, and this optimization problem is solved by JLRS algorithm to generate an initial ISAR image from which the random noise has been eliminated. The initial image is then subjected to further refinement using the Anisotropic Spatial Total Variation (ASTV) processing, ultimately yielding the final ISAR image with stripe interference removed. To validate the effectiveness of the LR-ASTV algorithm, ISAR imaging experiments based on simulated and measured data at different SNRs are completed, and the imaging results of the LR-ASTV algorithm are compared with those of other three algorithms including the LSM-ME2 algorithm, the JLRS algorithm and the LRPB algorithm. It can be found that the LR-ASTV algorithm has obvious superiority in suppressing random noise and removing stripe interference, and can provide ISAR images of higher clarity. The quality evaluation for ISAR images also shows that the LR-ASTV algorithm has lower image entropy index and higher image contrast index than the other three ISAR imaging algorithms. [ABSTRACT FROM AUTHOR]

Dynamic point clouds can effectively describe 3D objects and natural scenes, providing users with an immersive visual experience, but their huge amount of data requires efficient compression tools. To this end, the video-based point cloud compression (V-PCC) standard was developed, however, it may lead to cracks in the compressed point cloud at low bitrates. To solve the problem, this paper proposes a cracks-suppression perceptual geometry coding method for dynamic point cloud. Firstly, considering that the edge regions of 2D geometry patches in V-PCC are prone to cracks, a prior knowledge based crack region detection and preprocessing scheme is designed to reduce cracks. Secondly, considering the unique perceptual geometry characteristics of point cloud, a projected geometry curvature based structural similarity (PGC-SSIM) is proposed to evaluate geometry quality of point clouds, which contains the geometric perception information of point cloud, and is more consistent with the human visual perception. Finally, based on the PGC-SSIM, an adaptive quantization parameter adjustment strategy is designed for rate-distortion optimization in geometry coding of dynamic point clouds. The experimental results show that the proposed method can effectively reduce cracks in the compressed point cloud without reducing the coding performance compared to the V-PCC anchor. Moreover, the presented PGC-SSIM can be used to improve the visual quality of the compressed point cloud. [ABSTRACT FROM AUTHOR]

Time-frequency methods, which allow us to observe the seismic signal in time and frequency domains simultaneously, can efficiently discern the seismic response features of geological bodies in different frequency bands. In this paper, a newly developed time-frequency method named second order multi-synchrosqueezing transform (SMSST) is introduced to analyze seismic data. SMSST combines the second-order instantaneous frequency estimation and the iterative idea of multi-synchrosqueezing transform to approximate the true instantaneous frequency trajectory. It solves the problems of non-reassignment points and the sensitivity to noise at the same time. Synthetic example is employed to validate the superiority of SMSST in time-frequency analysis. Results obtained from four different methods show that SMSST offer the best performance in terms of energy-concentration and noise robustness. Applications on field data further demonstrate that SMSST-based method is suitable and effective to highlight geological information and stratigraphic features compared to the previous techniques. The capabilities of SMSST for accurate reservoir localization with high resolution, making it a promising and potential tool in seismic interpretation. [ABSTRACT FROM AUTHOR]

Estimating noise covariance matrices and suppressing outliers simultaneously is a huge challenge because they are coupled. In order to solve the nonlinear filtering problem with unknown non-Gaussian noise, the maximum correntropy criterion (MCC) is used to suppress the outliers, while the variational Bayesian (VB) technique is used to estimate the one-step prediction error covariance matrix (PECM) and the measurement noise covariance matrix (MNCM), and the nonlinear problem is solved by the third-order spherical radial cubature rule. In this paper, nominal measurement is constructed to eliminate the influence of outliers on the recursive estimation of MNCM. Through these operations, the variational Bayesian and maximum correntropy based cubature Kalman filter (VBMCCKF) and the maximum correntropy cubature Kalman filter (MCCKF) are derived. The interacting multiple model (IMM) fusion framework is used to promote the collaborative work of VBMCCKF and MCCKF, thus a novel filter is proposed in this paper. The simulation verifies the validity and universal applicability of the proposed filter. The estimation error of the proposed filter in spacecraft autonomous celestial navigation is about 30% less than that of the existing filter with the best performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of direction of arrival (DOA) estimation by exploiting the sparsity enforced recovery technique for co-prime arrays, which can increase the degrees of freedom. To apply the sparsity based technique, the discretization of the potential DOA range is required and every target must fall on the predefined grid. Off-grid target can highly deteriorate the recovery performance. To the end, this paper takes the off-grid DOAs into account and reformulates the sparse recovery problem with unknown grid offset vector. By introducing a convex function majorizing the given objective function, an iterative approach is developed to gradually amend the offset vector to achieve final DOA estimation. Numerical simulations are provided to verify the effectiveness of the proposed method in terms of detection ability, resolution ability and root mean squared estimation error, as compared to the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed-size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies. [ABSTRACT FROM AUTHOR]

In this paper, we design a low-complexity direction-of-arrival (DOA) estimation algorithm based on the unitary approximate message passing (UAMP) and Bernoulli-Gaussian (BG) prior. We first show that the estimation of DOA can be transferred into a sparse signal recovery problem, where we turn to UAMP with damping technique to solve this problem. Furthermore, we assume the BG prior on the sparse vector to be estimated, resulting in the fast estimation of the positions of non-zero elements. Moreover, expectation maximum (EM) is leveraged to automatically learn the BG parameters. Compared to the state-of-the-art UAMP-based algorithm with sparse Bayesian learning (SBL), the proposed approach can achieve the same DOA mean square error (MSE) performance with a much faster convergence speed. [ABSTRACT FROM AUTHOR]

Orthogonal polyphase sequence sets with desirable properties have numerous applications in sensing fields, e.g., a monostatic radar transmitting such waveforms can effectively counter the coherent repeater jamming (CRJ). However, due to imperfections in practical systems, the delicately designed sequences may suffer significant distortion and reduction in performance when transmitted as radar waveforms. In this paper, we focus on the problem of designing polyphase sequence sets with good correlation properties under spectral distortion. To address this issue, we first introduce a new metric called distortion correlation properties (DCorr), which quantifies the quality of such sequence sets. Subsequently, a majorization-minimization (MM)-based iterative algorithm is developed to solve the problem of minimizing DCorr. A number of numerical examples are provided to demonstrate the effectiveness of the proposed method in improving the correlation properties of polyphase sequence sets under spectral distortion. The results also validate that our proposed algorithms can effectively mitigate the performance reduction caused by the imperfections in practical radar systems simply. [ABSTRACT FROM AUTHOR]

Dictionary learning is crucially important for sparse representation of signals. Most existing methods are based on the so called synthesis model, in which the dictionary is column redundant. This paper addresses the dictionary learning and sparse representation with the so-called analysis model. In this model, the analysis dictionary multiplying the signal can lead to a sparse outcome. Though it has been studied in the literature, there is still not an investigation in the context of dictionary learning for nonnegative signal representation, while the algorithms designed for general signal are found not sufficient when applied to the nonnegative signals. In this paper, for a more efficient dictionary learning, we propose a novel cost function that is termed as the summation of blocked determinants measure of sparseness (SBDMS). Based on this measure, a new analysis sparse model is derived, and an iterative sparseness maximization scheme is proposed to solve this model. In the scheme, the analysis sparse representation problem can be cast into row-to-row optimizations with respect to the analysis dictionary, and then the quadratic programming (QP) technique is used to optimize each row. Therefore, we present an algorithm for the dictionary learning and sparse representation for nonnegative signals. Numerical experiments on recovery of analysis dictionary show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A joint approach is developed in this paper to simultaneously deal with the problem of track-to-track association and sensor registration at the track level. In previous research, it is usually supposed that sensor biases are directly imposed on local estimates, and only relative biases of sensors can be estimated. However, for some practical sensors such as the radar sensor, the measurement process is implemented in the local polar coordinate system. Thus, sensor biases are imposed on sensor measurements and included implicitly in local estimates represented in the global Cartesian coordinate system. In our previous work, a pseudo-measurement equation based on the first-order Taylor series expansion was derived revealing the relationship explicitly between local estimates and sensor biases. In this paper, by assuming that sensor biases are imposed on the original sensor measurements, we construct a novel mixed integer nonlinear programming (MINLP) model in the maximum likelihood rule. The model serves to determine the correspondence between local tracks and provide an access to estimate the absolute sensor biases in a recursive way. Several heuristic solution methods, including ‘Single-start’, ‘Gaussian Multi-start’, ‘K-best’ are implemented to handle the resulting MINLP model. Performance comparisons and analyses are made to illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

With the aim of retrieving high-quality images from corrupted versions, image restoration meets the extensive demand of application scenarios. State-of-the-art methods solve this problem by means of designing convolution blocks in multistage architectures. However, the existing methods blindly extract and fuse features without considering which parts of the features are more effective for image restoration. In this paper, we propose a significance-wise mechanism with the goal of identifying the importance degree of each region in the feature representation and providing crucial feature information. Through the calculation of nonlinear function in the significance-wise mechanism, the region which is more conducive to image restoration will get higher importance values. Two important types of information are generated within the attention mechanism, including: (i) feature-sufficient maps with abundant feature representations, and (ii) significance-wise maps with the importance degree of patch information in the corrupted images. With the coordination of feature-sufficient maps and significance-wise maps, the network architecture can focus higher attention on crucial parts of feature information. Furthermore, we design a multistage feature fusion block with the significance-wise mechanism. Compared with existing attention mechanisms, our significance-wise mechanism has the ability to identify and generate crucial feature representation for multiple image restoration tasks. Due to the novel design of the network, we successfully implement multiple restoration tasks only by fine-tuning the number of channels once on a network. Abundant quantitative and qualitative experiments demonstrate that the effective image restoration network (EIRN) outperforms existing state-of-the-art algorithms on eleven datasets across a series of image restoration tasks, including image deblurring, denoising, super-resolution, and deraining. The source code and pretrained models will be available at https://github.com/XinyueZhangqdu/EIRN. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the application of Chebychevʼs approximation theory to IIR digital filter frequency response (FR) approximation. It explores the properties of the frequency response of IIR digital filters as a nonlinear complex approximating function; IIR digital filter frequency response is used to approximate a prescribed magnitude and phase responses. The approximation problem is closely related to optimization. If the set of approximating functions is non-convex, the optimization problem is difficult and may converge to a local minimum. The main results presented in the paper are proposing a convex stability domain by introducing a condition termed “sign condition” and characterization of the best approximation by the Global Kolmogorovʼs Criterion (GKC). The Global Kolmogorovʼs Criteria is shown to be also a necessary condition for the approximation problem. Finally, it is proved that the best approximation is a global minimum. The sign condition can be incorporated as a constraint in an optimization algorithm. [Copyright &y& Elsevier]

Abstract The development of compressive sensing in recent years has given much attention to sparse signal recovery. In sparse signal recovery, spike and slab priors are playing a key role in inducing sparsity. The use of such priors, however, results in non-convex and mixed integer programming problems. Most of the existing algorithms to solve non-convex and mixed integer programming problems involve either simplifying assumptions, relaxations or high computational expenses. In this paper, we propose a new adaptive alternating direction method of multipliers (AADMM) algorithm to directly solve the suggested non-convex and mixed integer programming problem. The algorithm is based on the one-to-one mapping property of the support and non-zero element of the signal. At each step of the algorithm, we update the support by either adding an index to it or removing an index from it and use the alternating direction method of multipliers to recover the signal corresponding to the updated support. Moreover, as opposed to the competing "adaptive sparsity matching pursuit" and "alternating direction method of multipliers" methods our algorithm can solve non-convex problems directly. Experiments on synthetic data and real-world images demonstrated that the proposed AADMM algorithm provides superior performance and is computationally cheaper than the recently developed iterative convex refinement (ICR) and adaptive matching pursuit (AMP) algorithms. [ABSTRACT FROM AUTHOR]

Abstract This paper considers the problem of suppressing complex-jamming, which contains sidelobe blanket jammings (SLJs), multiple near-mainlobe blanket jammings (multiple-NMLJs) and self-defensive false target jamming (SDJ). We propose a blind source separation (BSS)-based space–time multi-channel algorithm for complex-jamming suppression. The space–time multi-channel consists of spatial multiple beams and temporal multiple adjacent pulse repetition intervals (PRIs). The source signals can be separated by the BSS, owing to their statistical independence. The real target and SDJ can then be obtained by the pulse compression approach, distinguished by echo identification simultaneously. A remarkable feature of the proposed approach is that it does not require prior knowledge about real target or jammings, and it is easy to implement for engineering applications. [ABSTRACT FROM AUTHOR]

• The proposed approach satisfies the need for precise focusing of spatial energy and, at the same time, meets spectral compatibility requirements. • The approach with a strict convergence and low computational complexity is proposed to solve the joint optimization problem. • A new update method for similarity constraint is proposed to utilize the freedom degree of signal space better. This paper aims to design multiple-input multiple-output (MIMO) radar transmit waveform that is able to focus energy on the target space-frequency domains while avoiding specific space-frequency domains for communicational devices. Specifically, the waveform design is recast as a joint optimization problem wherein the interference-to-signal energy ratio cost function turns out to be non-convex and non-smooth. In order to solve such an optimization problem, in this paper, an iterative optimization algorithm is proposed, wherein each iteration the majorization minimization (MM) scaling is firstly performed to the logarithmic cost function, and then the best alternative performance waveform vector is analytically achieved. As a result, the proposed algorithm can form a deep radiation energy null in the space-frequency domain where the communicational devices are expected to be located, and at the same time develop a higher radiation energy gain in specific target areas. The computational complexity of the proposed approach is quadratic to the number of array elements and snapshots, which shows potential for practical applications. Finally, the performance of the proposed algorithm has been verified via simulation experiments. [ABSTRACT FROM AUTHOR]

In this paper, we study the regularization l p -norm minimization problem to recover the sparse signals. We first prove that every global optimal solution to the regularization l p -norm minimization problem also solves the l 0 -norm minimization problem if the certain conditions are satisfied, and then generate a truncated approximated difference algorithm to recover the sparse signals. At last, we provide some numerical simulations to test the performance of the truncated approximated difference algorithm, and the numerical results show that the proposed algorithm performs effectively in recovering the sparse signals compared with some state-of-art methods. [ABSTRACT FROM AUTHOR]

In underwater acoustics, compressive sensing was applied to the separation of multiple raypaths based on direction of arrival (DOA) estimation. While it is impossible to separate raypaths directly without eliminating the interference between raypaths. This paper proposes a subspace-based higher-order compressive sensing algorithm, which enables separating raypaths interrupted by colored noise. In the algorithm, a trispectrum matrix is first calculated from the signal received on the reception array. To improve the robustness against noise, the trispectrum matrix is decomposed into signal and noise subspaces using an eigenvalue decomposition. Then, a subspace-based higher-order convex optimization problem is designed based on the signal subspace. The separation of raypaths is cast as the optimization problem solved in a greedy strategy approximation. The results obtained in both simulations and experiments show that the present method not only achieves the more precise separation of raypaths, but it also provides better suppression of noise as compared to the other algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of radar detection of distributed targets embedded in unknown compound-Gaussian clutter. To solve the problem, we first derive two detectors for fixed covariance matrix according to Rao and Wald tests with the use of order statistics theory. Then, the unknown covariance matrix is estimated by the approximate maximum likelihood (AML) estimation. Numerical examples through simulated and real data show that the Wald test-based detector has a higher probability of detection (PD) than existing detectors. [ABSTRACT FROM AUTHOR]

In this paper, an improved range-interpolation-free polar format algorithm (PFA) is proposed for the bistatic synthetic aperture radar (SAR). As an alternative solution to the range spatial frequency resampling of the classic bistatic PFA, the conventional range-frequency scaling transformation (CRST) provides a further explanation of the range resampling of the bistatic PFA from the perspective of range-azimuth decoupling. Nevertheless, CRST cannot be taken as the perfect equivalence to the range resampling of the PFA for its inalterable output spacing of the range spatial frequency. Besides, CRST can be implemented by the principle of chirp scaling (PCS). The scaled range frequency may exceed the observed frequency band, which will cause range spectrum aliasing. To solve the above problems, we propose an improved range-frequency scaling transformation (IRST), which has the same alterable range spatial frequency output spacing as the classic PFA. Further, the PCS-based IRST is also given. The aliased spectrum localization criterion is defined to avoid spectrum aliasing according to the relation between the range frequency and the observed frequency band after IRST. Analyses and simulations are executed to demonstrate the effects of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, reconfigurable intelligent surfaces (RISs) are broadly used due to their flexibility and cost-effectiveness in controlling the wireless environment with low-cost passive reflecting elements. In addition, RIS-assisted systems offer extremely power-efficient alternatives to conventional multi-antenna amplify-and-forward relaying systems. This paper presents an RIS-assisted secure wireless sensor network (WSN) that consists of multiple sensor nodes, an eavesdropper, a friendly jammer, and a sink node. The RIS helps to improve the direct link between the sensor nodes and the sink node. Meanwhile, the jammer and the RIS deteriorate the wiretap channel for the eavesdropper. More specifically, we aim to maximize the secrecy rate through the joint optimization of the RIS phase shifts, sensor scheduling strategy and jamming power. At first, the scheduling strategy is selected, and then the problem is solved using the block coordinate descent (BCD) method. Simulation results show the significant improvement in the secrecy performance achieved through the proposed RIS-assisted system. • Proposed RIS-assisted WSNs with a friendly jammer for enhanced security. • Utilize scheduling scheme based on energy level & channel state info. • Formulate secrecy rate maximization problem & closed-form solution. • Optimize jamming power to avoid negative effects on direct transmission. • Simulations show significant enhancement in secrecy performance. [ABSTRACT FROM AUTHOR]

Although the interval type-2 fuzzy c -means clustering algorithm (IT2FCM) can well represent the uncertainty in data, there remain some problems to be solved: how to initialize cluster centers and how to determine fuzzifiers. In order to solve these issues of IT2FCM for color image segmentation, a pareto-based interval type-2 fuzzy c -means with multi-scale just noticeable difference color histogram (PIT2FC-MJND) is proposed in this paper. A multi-scale just noticeable difference (JND) color histogram is firstly constructed by using many distance thresholds and utilized to provide initial cluster centers. Then, a modified type-reduction and de-fuzzification mechanism on this multi-scale JND color histogram is designed for updating membership functions and cluster centers. Moreover, a pareto-based strategy for determining the combination of fuzzifiers is presented by using a global fuzzy compactness function and a fuzzy separation function which are based on the constructed multi-scale JND color histogram. The experimental results on real, Berkeley and Weizmann Images confirm the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

X-ray security inspection machine is currently the most widely used security inspection technology in our country. It is widely used in urban rail transit, railways, airports, key venues, logistics delivery and other scenarios. Using artificial intelligence technology to assist front-line security inspectors in X-ray security inspection can effectively reduce problems such as missed reports due to personnel fatigue or inattention. However, in actual scenes, the detection accuracy is not high due to the diversity of item types, the particularity of imaging angles, and the limitations of detection algorithms. In order to solve related problems, we propose MAM Faster R-CNN. First, in order to expand the receptive field of the feature map and effectively extract the regional features of the target object with shape distortion in the feature map, we propose the Malformed Attention Module (MAM). Secondly, the Large Kernel Attention (LKA) block is used to connect the corresponding backbone output feature layer to use the adaptive selection feature of self-attention module to better focus on the effective feature information in the feature map. Finally, for the neck part, we replace feature pyramid network (FPN) with path aggregation network (PAN), and add the Conv-MLP block to the bottom-up feature fusion part on the right side of the PAN network to reduce the loss of some low-level details. In this paper, the Faster R-CNN benchmark model with ResNet50 as the backbone network is used to evaluate our proposed MAM Faster R-CNN model on HiXray and OPIXray datasets, and the results show that it outperforms the SOTA detection method. We do ablation experiments on the HiXray dataset, and the results show that our proposed MAM Faster R-CNN improves about 2.0%, compared to baseline model (Faster R-CNN). [ABSTRACT FROM AUTHOR]

Low-light images suffer from poor visibility, severe noise, low contrast, and low brightness. To overcome these issues, many image enhancement methods have been proposed. Few techniques solve these problems simultaneously. This paper presents a low-light image enhancement method. The proposed method first applies the HSV (Hue, Saturation, Value) transform to the input image. Here, a multi-scale decomposition of the Sharpening-Smoothing Image Filter (SSIF) is proposed to obtain approximation and detail sub-images of the V component. After the decomposition process, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is applied to the final approximation image to provide higher contrast. The detail sub-images are amplified and added to the enhanced approximation image to reconstruct the enhanced V component. Finally, inverse HSV transform is applied to the enhanced V component and H, S components to obtain the enhanced image. The experimental results show that the proposed method provides better visual quality and more natural colors than the compared state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, the design of phase-coded sequences with good aperiodic autocorrelation properties including peak sidelobe level and integrated sidelobe level for radar systems is considered. The problem is formulated as a rank-one constraint optimization at the design stage. To tackle the resulting non-convex and generally NP-hard optimization problem, an iterative algorithm is developed based on the sequential rank-one constraint relaxation framework, which guarantees a local optimal rank-one solution. The proposed method requires solving a convex problem in each iteration. The procedure can be initialized by dropping the rank-one constraint or using a good sequence in terms of autocorrelation properties. Simulation results show that using the proposed method for finding phase-coded sequences leads to better peak sidelobe ratio and integrated sidelobe ratio for polyphase and binary phase codes compared to previous works in the literature. [ABSTRACT FROM AUTHOR]

The contrast enhancement with reversible data hiding (CERDH) has become a hot topic in many fields. At present, most schemes applying CERDH methods to color images have the problem of color distortion. To solve this problem, this paper proposes a CERDH method for color images based on constant hue plane. It firstly separates the color images into min, mid and max channels. Then, a novel preprocessing is proposed to maintain the distribution of histogram for original image while vacate room for data hiding. After that, the mid channel is used for data hiding, and min and max channels are hue-preserved to avoid color distortion. Finally, the three channels compose a contrast-enhanced color image. The experimental results show that the proposed method achieves the best enhanced results based on CIEDE2000 compared with the state-of-the-art methods, and color distortion is also greatly reduced. [ABSTRACT FROM AUTHOR]

This paper proposes a continuous time irrational filter structure via a set of the fractional order Gammatone components instead of via a set of integer order Gammatone components. The filter design problem is formulated as a nonsmooth and nonconvex infinite constrained optimization problem. The nonsmooth function is approximated by a smooth operator. The domain of the constraint functions is sampled into a set of finite discrete points so the infinite constrained optimization problem is approximated by a finite constrained optimization problem. To find a near globally optimal solution, the norm relaxed sequential quadratic programming approach is applied to find the locally optimal solutions of this nonconvex optimization problem. The current or the previous locally optimal solutions are kicked out by adding the random vectors to them. The locally optimal solutions with the lower objective functional values are retained and the locally optimal solutions with the higher objective functional values are discarded. By iterating the above procedures, a near globally optimal solution is found. The designed filter is applied to perform the denoising. It is found that the signal to noise ratio of the designed filter is higher than those of the filters designed by the conventional gradient descent approach and the genetic algorithm method, while the required computational power of our proposed method is lower than those of the conventional gradient descent approach and the genetic algorithm method. Also, the signal to noise ratio of the filter with the fractional order Gammatone components is higher than those of the filter with the integer order Gammatone components and the conventional rational infinite impulse response filters. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel imaging and Doppler parameter estimation algorithm for ground maneuvering targets. Since the cross-track acceleration will induce the quadratic chirp rate (third-order phase) in the phase history, it may cause the maneuvering target severely smeared in the Doppler domain. To obtain a well-focused target imaging result, the quadratic chirp rate must be estimated accurately. Though cubic phase function (CPF) is efficient in estimating the parameters of a single maneuvering target, it may suffer from the identifiability problem when dealing with multiple maneuvering targets. To address these issues, an axis mapping (AM) based coherently integrated cubic phase function (CICPF) algorithm is proposed. This algorithm consists of two stages. Firstly, the linear chirp rate migration (i.e. quadratic chirp rate) of target in the time and chirp-rate domain is corrected by AM. After that, a dechirping technique is utilized to coherently integrate the auto-terms, and suppress the cross-terms and spurious peaks. Compared with several existing quadratic chirp rate estimation approaches, AM based CICPF (AMCICPF) algorithm can acquire lower signal-to-noise ratio threshold and estimate the centroid frequency, chirp rate and quadratic chirp rate of maneuvering target simultaneously. By compensating the chirp rate and quadratic chirp rate, a finely focused maneuvering target imaging can be obtained. Both simulated and real data processing results show that the AMCICPF algorithm serves as a good candidate for maneuvering target Doppler parameter estimation and imaging. [ABSTRACT FROM AUTHOR]

The low rank tensor completion (LRTC) problem has attracted great attention in computer vision and signal processing. How to acquire high quality image recovery effect is still an urgent task to be solved at present. This paper firstly proposes a new tensor L 2 , 1 norm minimization model (TLNM) that integrates sum nuclear norm (SNN) method, differing from the classical tensor nuclear norm (TNN)-based tensor completion method, with L 2 , 1 norm and QR decomposition for solving the LRTC problem. To improve the utilization rate of the local prior information of the image, a total variation (TV) regularization term is introduced, resulting in another new class of tensor L 2 , 1 norm minimization with total variation model (TLNMTV). Both proposed models are convex and therefore have global optimal solutions. Moreover, we adopt the Alternating Direction Multiplier Method (ADMM) to obtain the closed-form solution of each variable, which makes the algorithm be able to be efficiently implemented. Numerical experiments show that the two proposed algorithms are convergent and outperform compared methods. In particular, our method significantly outperforms the compared methods when the sampling rate is 2.5% for hyperspectral images. [ABSTRACT FROM AUTHOR]

Demosaicking is a process used to estimate missing color values from the subsampled color filter array (CFA) image to reduce the cost and volume of a digital still camera. However, by sampling theory, it is known that subsampling a signal causes overlaps of signals in the frequency domain, which is known as aliasing. Most current demosaicking processes cannot completely solve aliasing problem resulting in aliasing artifacts such as false colors and zipper effects. In this paper, we propose an algorithm to remove these aliasing artifacts in demosaicked color images. A luminance image with minimum aliasing is obtained from the CFA image by using a low-pass kernel with cutoff frequencies determined by an approximate model for the Fourier spectrum. An aliasing map is computed by analyzing subband signals of the CFA image based on the high correlation of the high-frequencies of the luminance and color channels. Then, a least squares of the luminance acquisition processes is used to design a cost function with the aliasing map to remove the aliasing artifacts. The experiments demonstrate that the proposed algorithm sufficiently removes aliasing artifacts and improves the quality of the color images. [ABSTRACT FROM AUTHOR]

Image denoising methods have different denoising performance in both spatial and transform domains, and each method has its relative advantages and inherent shortcomings compared with other methods. A very intuitive idea is to find that an effective fusion method that can combine with the advantages of different denoising methods. In this paper, we propose a novel fusion method based on the fractional Fourier transform and apply it to image denoising problem. Our method is mainly divided into three steps: Firstly, a pre-estimation is made by any two denoising method separately in the spatial domain. Secondly, using these two estimated results as well as their Fourier transform, twice Fourier transform and three times Fourier transform, we obtain a fused result in the fractional Fourier transform domain. Thirdly, the inverse fractional Fourier transform and the modulus operation are used to obtain the final fusion result. Obviously, this approach is the fusion method in four different domains. Experimental results on benchmark test images demonstrate that the proposed method outperforms state-of-the-art stand-alone methods as: BM3D, DDID, MLP, EPLL and also superior to the fusion methods such as classic wavelet fusion method, PCA fusion method and the state-of-the-art CIEM fusion method in terms of quantity value such as the peak signal to noise ratio (PSNR), the structural similarity (SSIM), and visual quality. [ABSTRACT FROM AUTHOR]

Ptychography is a coherent diffractive imaging technique that surpasses the resolution limitations of typical X-ray optics. It generates label-free, high-contrast quantitative amplitude and phase images without requiring prior knowledge. How to quickly recover high-fidelity amplitude and phase contrast images of an authentic sample is a challenge, however, due to the expensive computation that precludes real-time imaging. This paper proposes a framework with a double-branch network, Π-Net, to solve this complex problem. It can simultaneously and robustly reconstruct the object's phase and amplitude information from a degraded image. By analyzing the performance of various activation functions, we find that the activation function plays a crucial role in phase retrieval. Numerical experiments demonstrate the robustness of Π-Net, and our reconstruction results are superior to those of state-of-the-art methods. In addition, ablation studies have revealed that the negative output of neurons can contribute to improved reconstruction outcomes. • We proposed excellent double-branch network architecture with potential and wide application. • It has strong robustness and tidy processing. • We study the vital role of negative output in architecture. [ABSTRACT FROM AUTHOR]

Secret image sharing is a method to decompose a secret image into shadow images (shadows) so that only qualified subset of shadows can be used to reconstruct the secret image. Usually all shadows have the same importance. Recently, an essential SIS (ESIS) scheme with different importance of shadows was proposed. All shadows are divided into two group: essential shadows and non-essential shadows. In reconstruction, the involved shadows should contain at least a required number of shadows, including at least a required number of essential shadows. However, there are two problems in previous ESIS scheme: unequal size of shadows and concatenation of sub-shadow images. These two problems may lead to security vulnerability and complicate the reconstruction. In this paper, we propose a novel ESIS scheme based on derivative polynomial and Birkhoff interpolation. A single shadow with the same-size is generated for each essential and non-essential participant. The experimental results demonstrate that our scheme can avoid above two problems effectively. [ABSTRACT FROM AUTHOR]

Multiplicative noise removal is a key issue in image processing problem. While a large amount of literature on this subject are total variation (TV)-based and wavelet-based methods, recently sparse representation of images has shown to be efficient approach for image restoration. TV regularization is efficient to restore cartoon images while dictionaries are well adapted to textures and some tricky structures. Following this idea, in this paper, we propose an approach that combines the advantages of sparse representation over dictionary learning and TV regularization method. The method is proposed to solve multiplicative noise removal problem by minimizing the energy functional, which is composed of the data-fidelity term, a sparse representation prior over adaptive learned dictionaries, and TV regularization term. The optimization problem can be efficiently solved by the split Bregman algorithm. Experimental results validate that the proposed model has a superior performance than many recent methods, in terms of peak signal-to-noise ratio, mean absolute-deviation error, mean structure similarity, and subjective visual quality. [ABSTRACT FROM AUTHOR]

Location awareness is now becoming a vital requirement for many practical applications. In this paper, we consider passive localization of multiple targets with one transmitter and several receivers based on time of arrival (TOA) measurements. Existing studies assume that positions of receivers are perfectly known. However, in practice, receivers' positions might be inaccurate, which leads to localization error of targets. We propose factor graph (FG)-based belief propagation (BP) algorithms to locate the passive targets and improve the position accuracy of receivers simultaneously. Due to the nonlinearity of the likelihood function, messages on the FG cannot be derived in closed form. We propose both sample-based and parametric methods to solve this problem. In the sample-based BP algorithm, particle swarm optimization is employed to reduce the number of particles required to represent messages. In parametric BP algorithm, the nonlinear terms in messages are linearized, which results in closed-form Gaussian message passing on FG. The Bayesian Cramér–Rao bound (BCRB) for passive targets localization with inaccurate receivers is derived to evaluate the performance of the proposed algorithms. Simulation results show that both the sample-based and parametric BP algorithms outperform the conventional method and attain the proposed BCRB. Receivers' positions can also be improved via the proposed BP algorithms. Although the parametric BP algorithm performs slightly worse than the sample-based BP method, it could be more attractive in practical applications due to the significantly lower computational complexity. [ABSTRACT FROM AUTHOR]

With numerous antennas, the massive multiple-input multiple-output (MIMO) system can precisely point to the user equipments (UEs) with narrow beams. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system, since UEs may move out of the beam coverage. Frequent CSI feedback and beam adjustment help to track mobile UEs. However, a significant amount of transmission and computation overhead is incurred. In this paper, we consider a mobility adaption problem in massive MIMO systems with infrequent CSI feedback. We propose a beam design method to use one beam to serve multiple transmissions, thus reducing the overhead of frequency beam adjustment. An optimization problem is formulated that maximizes the overall system sum rate during one beam adjustment, i.e., during the entire time interval between CSI feedback. We assume that each transmission antenna has a peak power constraint, as the power amplifier in each branch limits the transmit power. A fractional programming based algorithm is proposed to solve the problem. To further reduce the computation overhead, a sub-optimal solution is obtained. Simulation results show that the proposed algorithms achieve satisfactory performance with little transmission and computation overhead compared to existing algorithms. [ABSTRACT FROM AUTHOR]

The mutual coupling between elements degrades the direction-of-arrival (DOA) estimation performance. This paper considers jointly estimating the DOA and mutual coupling. Firstly, based on the fact that the noiseless array output without mutual coupling consists of complex exponential sums, an annihilation relation is obtained. Then, an optimization problem is proposed by combining a cost function derived from the maximum likelihood principle and a constraint formed by the annihilation relation. The optimization problem is solved by the sequential quadratic programming (SQP) method, where the Jacobian matrix is derived via exploiting the symmetric Toeplitz structure of the mutual coupling matrix. At last, a post-processing method is proposed for further improving the accuracy of mutual coupling estimation. The new method can handle single snapshot estimation, and it is gridless while utilizing the whole aperture. It is also insensitive to overestimation of the degree of mutual coupling. The method has been verified by simulations and compared with representative methods regarding accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Polarization-sensitive array (PSA) processing works well in navigation system applications for interference mitigation. However, the fixed configuration of conventional PSA increases the computational burden of subsequent adaptive array processing and can even be inefficient and redundant in some cases. Furthermore, since the radio frequency (RF) front end is more costly than the antenna, it is urgent to exploit a PSA reconfiguration method that can lower the cost of array design and achieve sufficiently good anti-interference performance when employing fewer RF front ends and antennas. In this paper, a PSA reconfiguration technique maximizing the signal to interference plus noise ratio (SINR) is presented to solve the problem. Combining with adaptive processing, we derive the formula between the SINR and the polarization-spatial correlation coefficient (PSCC), thereby building the mathematical model with minimizing PSCC as the objective function. Then, the lower bound of the optimal PSCC, gained by the Lagrange Dual Relaxation method, is utilized to guide the selection of the antenna number. Finally, we adopt an iterative correlation measurement (ICM) approach to confirm the antenna element position of the optimal subarray with the minimum PSCC. Numerical simulations illustrate the effectiveness and correctness of the proposed technique and derived theoretical results. [ABSTRACT FROM AUTHOR]

As a classical secret sharing technique, (k , n) threshold secret image sharing mainly contains the shadow generation phase and the image reconstruction phase. The sensitive secret image is encrypted into n shadows by the dealer in the shadow generation phase, and the secret image can be reconstructed completely by the participants when the number of shadows meet or exceed the threshold k. Previous progressive or hierarchical secret image sharing schemes have some disadvantages (e.g., inflexibility and inefficiency). To solve this problem, we propose a progressive and hierarchical image segmentation-sharing scheme (referred to as PHISS) in this paper. First, the important content region of the sensitive image (called by the target sub-image) is generated by using the proposed image segmentation algorithm. Here the target sub-image refers to the part with the largest amount of information, for instance, the human face in private photo, the pathological region in medical image and the aircraft in military image. Then, in the shadow generation phase, the target sub-image is encrypted into several shadows with hierarchy, and the hierarchies of the generated shadows in the background sub-image are equal. Last, in the reconstruction phase, the background sub-image can be reconstructed completely. The target sub-image has different quality with different number of shadows and different level of shadows. The image can be reconstructed losslessly if and only if all shadows involved. The experimental results and the comparison with similar works demonstrate the feasibility and the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers the problem of joint detection and tracking in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments by using received signal strength (RSS) measurements. A nonlinear target tracking model with multiple switching parameters has been proposed, in which multiple independent Markov chains are used to describe the switching of target maneuvers and the transition of LOS/NLOS measurements, respectively. Based on the proposed tracking model, a multi-sensor multiple model Bernoulli filter (MMBF) has been developed by employing the random finite set theory which can formulate the joint detection and tracking in a unified framework. To derive a closed-form expression to the MMBF, the Gaussian mixture implementations have been provided by applying the extended Kalman filter technique. A numerical example is provided involving tracking a maneuvering target by a sensor network with 30 nodes. Simulation results confirm the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

Total Variation (TV) regularization is a widely used convex but non-smooth regularizer in image restoration and reconstruction. Many algorithms involve solving a denoising problem as an intermediate step or in each iteration. Most existing solvers were proposed in the context of a specific application. In this paper, we propose a denoising method which can be used as a proximal mapping (denoising operator) for noises other than additive and Gaussian. We formulate the Maximum A-Posteriori (MAP) estimation in terms of a spatially adaptive and recursive filtering operation on the Maximum Likelihood (ML) estimate. The only dependence on the model is the ML estimate and the second order derivative, which are computed at the beginning and remain fixed throughout the iterative process. The proposed method generalizes the MAP estimation with a quadratic regularizer using an infinite impulse response filter, to the case with TV regularization. Due to the fact that TV is non-smooth and has spatial dependencies, the resulting filter after reweighted least squares formulation of the TV term, is recursive and spatially variant. The proposed method is an instance of the Majorization–Minimization (MM) algorithms, for which convergence conditions are defined and can be shown to be satisfied by the proposed method. The method can also be extended to image inpainting and higher order TV in an intuitively straight-forward manner. [ABSTRACT FROM AUTHOR]

This paper proposes a new scene change detection method that uses local directional coding for frame rate up-conversion. The directional coding technique converts the luminance value of the edge direction response for given frames to bit codes and calculates the bit code difference between the previous and current frames. Existing scene change detection methods usually calculate the difference between the histogram shapes or the luminance values between the previous and current frames to detect scene changes. Thus, they erroneously detect scene changes in a scene with moving objects or camera motion. The existing automatic thresholding-based method uses iterative operations to calculate the optimal threshold and determine the scene change using the difference between the calculated threshold values, and it therefore has a high computational cost. To solve these problems, the proposed method uses the difference between the bit codes from the eight edge response values between the previous and current frames to detect the initial scene change regions. In addition, the proposed method uses a refinement process on the detected initial scene change regions to enhance the scene change detection accuracy. The experimental results showed that the proposed method enhanced average F1 score to 0.5235 (a 126.36 % improvement) as compared with the benchmark methods. The average computation time per pixel of the proposed method also reduced to 13.3668 μs (an 87.39 % reduction) compared with the benchmark methods. • It converts luminance values for the edge directions into bit codes for FRUC. • Proposed method requires less computation to detect scene-changes. • Proposed post-processing successfully improves the detection accuracy. [ABSTRACT FROM AUTHOR]