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Vocal Tract Length Perturbation for Text-Dependent Speaker Verification With Autoregressive Prediction Coding IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-28 Achintya kr. Sarkar; Zheng-Hua Tan
In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision. Next, we explore the bottleneck (BN) feature extracted by training deep neural networks with a self-supervised learning objective, autoregressive predictive
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Efficient Synthesis of Filter-and-Sum Array With Scanned Wideband Frequency-Invariant Beam Pattern and Space-Frequency Notching IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-21 Liyang Chen; Yanhui Liu; Shiwen Yang; Y. Jay Guo
This work generalizes the Fourier transform (FT)-based frequency-invariant beamforming (FIB) method to the synthesis of scanned frequency-invariant (FI) beam pattern with space-frequency notching for an array with non-isotropic elements. Wideband FI pattern characteristics are described by using multiple reference sub-band FI patterns. By applying fast Fourier transform (FFT) on the combination of
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The NLMS Is Steady-State Schur-Convex IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-29 Anum Ali; Muhammad Moinuddin; Tareq Y. Al-Naffouri
In this work, we study the impact of input-spread on the steady-state excess mean squared error (EMSE) of the normalized least mean squares (NLMS) algorithm. First, we use the concept of majorization to order the input-regressors according to their spread. Second, we use Schur-convexity to show that the majorization order of the input-regressors is preserved in the EMSE. Effectively, we provide an
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Multiscale Optimal Filtering on the Sphere IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-02-02 Adeem Aslam; Zubair Khalid; Jason D. McEwen
We present a framework for the optimal filtering of spherical signals contaminated by realizations of an additive, zero-mean, uncorrelated and anisotropic noise process on the sphere. Filtering is performed in the wavelet domain given by the scale-discretized wavelet transform on the sphere. The proposed filter is optimal in the sense that it minimizes the mean square error between the filtered wavelet
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PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-11 Brendt Wohlberg; Przemek Wozniak
We present a new algorithm for estimating the Point Spread Function (PSF) in wide-field astronomical images with extreme source crowding. Robust and accurate PSF estimation in crowded astronomical images dramatically improves the fidelity of astrometric and photometric measurements extracted from wide-field sky monitoring imagery. Our radically new approach utilizes convolutional sparse representations
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Unsupervised Person Re-Identification Based on Measurement Axis IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-02-01 Jiahan Li; Deqiang Cheng; Ruihang Liu; Qiqi Kou; Kai Zhao
The main focus of unsupervised person re-identification is the clustering of unlabeled samples in the target domain. However, most existing studies neglected to mine the deep semantic information of the target domain and did not consider a better combination of the source domain and the target domain. In this letter, we not only consider the changes of the target domain within its own domain but also
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Feasibility of Joint Power Optimization of Multiple Source-Destinations in an AF Relay Network IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-28 Aziz Bahmani; Mehrzad Biguesh Bighesh
Joint power optimization of sources and relay in cooperative networks, while multiple source-destination (SD) pairs are utilizing a relay and also have direct transmission links, is addressed in this letter. In the assumed network each SD pair requires a minimum quality of service (QoS) in terms of its received SINR. We have shown that the resulted power control equations are quadratic due to coexistence
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MIND-Net: A Deep Mutual Information Distillation Network for Realistic Low-Resolution Face Recognition IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-21 Cheng-Yaw Low; Andrew Beng-Jin Teoh; Jaewoo Park
Realistic low-resolution (LR) face images refer to those captured by the real-world surveillance cameras at extreme standoff distances, thereby LR and poor in quality essentially. Owing to severe scarcity of labeled data, a high-capacity deep convolution neural networks (CNN) is hardly trained to confront the realistic LR face recognition (LRFR) challenge. We introduce in this letter a dual-stream
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New Mutually Orthogonal Complementary Sets With Non-Power-of-Two Lengths IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-26 Liying Tian; Xiaoshuai Lu; Chengqian Xu; Yubo Li
Mutually orthogonal complementary sets (MOCSs) have found a number of practical applications in wireless communications and radar owing to their perfect aperiodic auto-correlation and cross-correlation properties. Recently, toward the challenge of designing MOCSs with non-power-of-two lengths, direct constructions were presented by Wu et al. using generalized Boolean functions (GBFs). In this letter
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Ray-Space-Based Multichannel Nonnegative Matrix Factorization for Audio Source Separation IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-29 Mirco Pezzoli; Julio José Carabias-Orti; Maximo Cobos; Fabio Antonacci; Augusto Sarti
Nonnegative matrix factorization (NMF) has been traditionally considered a promising approach for audio source separation. While standard NMF is only suited for single-channel mixtures, extensions to consider multi-channel data have been also proposed. Among the most popular alternatives, multichannel NMF (MNMF) and further derivations based on constrained spatial covariance models have been successfully
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Distortion-Aware Monocular Depth Estimation for Omnidirectional Images IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-11 Hong-Xiang Chen; Kunhong Li; Zhiheng Fu; Mengyi Liu; Zonghao Chen; Yulan Guo
Image distortion is a main challenge for tasks on panoramas. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) network to estimate dense depth maps from indoor panoramas. First, we introduce a distortion-aware module to extract semantic features from omnidirectional images. Specifically, we exploit deformable convolution to adjust its sampling grids to geometric distortions
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Pruning by Training: A Novel Deep Neural Network Compression Framework for Image Processing IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-25 Guanzhong Tian; Jun Chen; Xianfang Zeng; Yong Liu
Filter pruning for a pre-trained convolutional neural network is most normally performed through human-made constraints or criteria such as norms, ranks, etc. Typically, the pruning pipeline comprises two-stage: first learn a sparse structure from the original model, then optimize the weights in the new prune model. One disadvantage of using human-made criteria to prune filters is that the design and
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Robust Minimum Error Entropy Based Cubature Information Filter With Non-Gaussian Measurement Noise IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-29 Minzhe Li; Zhongliang Jing; Henry Leung
In this letter, a robust minimum error entropy based cubature information filter is proposed for state estimation in non-Gaussian measurement noise. A new combined optimization cost is defined based on the error entropy. Through cubature transform, a statistical linearization regression model is constructed, and a new information filter is then developed by minimizing the error entropy based cost.
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GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-05 Qiang Wang; Chen Xu; Wenqi Zhang; Jingjing Li
This letter proposes a new travel time estimation model based on graph neural network (GraphTTE) to improve the accuracy of travel time estimation. We design a Multi-layer Spatiotemporal Graph frame (MSG), which consists of static network and dynamic networks, to fully consider the influence of traffic temporal characteristics and road network topological characteristics on travel time. Moreover, we
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Dark-Aware Network For Fine-Grained Sketch-Based Image Retrieval IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-11 Zhantao Yang; Xiaoguang Zhu; Jiuchao Qian; Peilin Liu
Fine-grained sketch-based image retrieval (FG-SBIR) is an emerging topic in high-level computer vision. Among existing methods, edge-only information based ones are convenient to use but incompetent in distinguishing among ambiguous samples. On the other hand, even though the methods using additional color information can significantly improve the retrieval performance, they are not convenient for
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Risk-Aware Multi-Armed Bandits With Refined Upper Confidence Bounds IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-28 Xingchi Liu; Mahsa Derakhshani; Sangarapillai Lambotharan; Mihaela van der Schaar
The classical multi-armed bandit (MAB) framework studies the exploration-exploitation dilemma of the decision-making problem and always treats the arm with the highest expected reward as the optimal choice. However, in some applications, an arm with a high expected reward can be risky to play if the variance is high. Hence, the variation of the reward should be considered to make the arm-selection
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DOA Refinement Through Complex Parabolic Interpolation of a Sparse Recovered Signal IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-14 Luca Pallotta; Gaetano Giunta; Alfonso Farina
This letter considers the design of a two-stage direction of arrival (DOA) scheme for radar systems. Precisely, at the first stage a sparse recovery approach is used to obtain both DOA and complex amplitude estimates of the incoming signal. Since the DOA is evaluated on a predefined grid of bins sampling the antenna azimuth mainbeam, at the second stage, a closed-form complex-valued parabolic interpolation
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Template Enhancement and Mask Generation for Siamese Tracking IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-31 Xiao Ke; Yuezhou Li; Yu Ye; Wenzhong Guo
Siamese tracking methods have become the focus of visual tracking in recent years. Advanced Siamese trackers perform well on certain benchmarks, but there are still some limitations. First, most Siamese trackers adopt the initial frame as a single template, which leads to underfitting and reduces the ability to predict instances. Second, mainstream trackers report a rectangular bounding box as a prediction
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Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-13 Taku Nonomura; Shunsuke Ono; Kumi Nakai; Yuji Saito
The randomized subspace Newton convex methods for the sensor selection problem are proposed. The randomized subspace Newton algorithm is straightforwardly applied to the convex formulation, and the customized method in which the part of the update variables are selected to be the present best sensor candidates is also considered. In the converged solution, almost the same results are obtained by original
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Reconstruction of Periodic Signals From Asynchronous Trains of Samples IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-14 Marek W. Rupniewski
A short train of equidistant samples rarely suffices to compute a reasonable approximation to a continuous-time signal. Hence, there arises a need for signal reconstruction methods that exploit multiple trains of samples. If the starting times of the trains are known, then stroboscopic techniques can be used for signal reconstruction. If these times are not given, i.e., in the case of asynchronous
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Online EM-Based Ensemble Classification With Correlated Agents IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-15 Emre Efendi; Berkan Dulek
A binary ensemble classification method that sequentially processes the data collected from multiple decision agents in the presence of parameter uncertainties is proposed. Agents are assumed to form correlated groups whose decisions are modeled as multivariate Bernoulli random vectors. The prior probabilities of the binary hypotheses and the corresponding probabilities of the outcomes under each hypothesis
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A Clustering-Based Approach for Designing Low Complexity FIR Filters IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-12 Mohammad H. Nassralla; Naeem Akl; Zaher Dawy
Low power requirements, real-time constraints and big data applications present several challenges to digital signal processing (DSP) that span multiple domains including filtering and frequency analysis. Design of digital filters that can efficiently process signals with low computational complexity is desirable and requires innovative approaches. In this paper, we present an efficient two-step design
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Sifting Convolution on the Sphere IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-12 Patrick J. Roddy; Jason D. McEwen
A novel spherical convolution is defined through the sifting property of the Dirac delta on the sphere. The so-called sifting convolution is defined by the inner product of one function with a translated version of another, but with the adoption of an alternative translation operator on the sphere. This translation operator follows by analogy with the Euclidean translation when viewed in harmonic space
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A Maximum-Likelihood TDOA Localization Algorithm Using Difference-of-Convex Programming IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-14 Xiuxiu Ma; Tarig Ballal; Hui Chen; Omar Aldayel; Tareq Y. Al-Naffouri
A popular approach to estimate a source location using time difference of arrival (TDOA) measurements is to construct an objective function based on the maximum likelihood (ML) method. An iterative algorithm can be employed to minimize that objective function. The main challenge in this optimization process is the non-convexity of the objective function, which precludes the use of many standard convex
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Hypersphere Fitting From Noisy Data Using an EM Algorithm IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-14 Julien Lesouple; Barbara Pilastre; Yoann Altmann; Jean-Yves Tourneret
This letter studies a new expectation maximization (EM) algorithm to solve the problem of circle, sphere and more generally hypersphere fitting. This algorithm relies on the introduction of random latent vectors having a priori independent von Mises-Fisher distributions defined on the hypersphere. This statistical model leads to a complete data likelihood whose expected value, conditioned on the observed
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Multiple Target Tracking With Unresolved Measurements IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-14 R. Blair Angle; Roy L. Streit; Murat Efe
A multiple target tracking filter is developed for merged measurement problems that arise with finite resolution sensors. The resulting combinatorial problem is incorporated directly in the joint likelihood function using analytic combinatorics techniques. The Bayesian filter is a sum of several terms that correspond one-to-one to the set of all feasible hypotheses about measurements and targets, i
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Entropy Optimized Deep Feature Compression IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-18 Benben Niu; Xiaoran Cao; Ziwei Wei; Yun He
This letter focuses on the compression of deep features. With the rapid expansion of deep feature data in various CNN-based analysis and processing tasks, the demand for efficient compression continues to increase. Product quantization (PQ) is widely used in the compact expression of features. In the quantization process, feature vectors are mapped into fixed-length codes based on a pre-trained codebook
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Energy-Efficient Distributed Learning With Coarsely Quantized Signals IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-13 Alireza Danaee; Rodrigo C. de Lamare; Vítor H. Nascimento
In this work, we present an energy-efficient distributed learning framework using low-resolution ADCs and coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost
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Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-06 Wei Peng; Jingang Shi; Guoying Zhao
Benefited from the powerful ability of spatial temporal Graph Convolutional Networks (ST-GCNs), skeleton-based human action recognition has gained promising success. However, the node interaction through message propagation does not always provide complementary information. Instead, it May even produce destructive noise and thus make learned representations indistinguishable. Inevitably, the graph
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Consensus-Based Distributed Computation of Link-Based Network Metrics IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-08 Zheng Chen; Erik G. Larsson
Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors. In this letter, we look into link-based network metrics which are polynomial functions of pair-wise node attributes defined over the links in a network. Different from node-based average
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Vector-to-Vector Regression via Distributional Loss for Speech Enhancement IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-08 Sabato Marco Siniscalchi
In this work, we leverage on a novel distributional loss to improve vector-to-vector regression for feature-based speech enhancement (SE). The distributional loss function is devised based on the Kullback-Leibler divergence between a selected target distribution and a conditional distribution to be learned from the data for each coefficient in the clean speech vector given the noisy input features
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Support Vector Machine-Based Blind Equalization for High-Order QAM With Short Data Length IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-12 Xiaobei Liu; Yong Liang Guan; Qiang Xu
In this paper, the problem of blind equalization of high-order quadrature amplitude modulation (QAM) signals is tackled by using a batch equalizer based on support vector regression (SVR). A new set of error functions weighted by neighborhood symbol decisions and augmented by generalized power factors $p$ and $q$ , are proposed to be used as the penalty terms in SVR, and the optimal values of $p$ and
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LFNet: Local Rotation Invariant Coordinate Frame for Robust Point Cloud Analysis IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-31 Hezhi Cao; Ronghui Zhan; Yanxin Ma; Chao Ma; Jun Zhang
Deep neural networks have achieved great progress in 3D scene understanding. However, recent methods mainly focused on objects with canonical orientations in contrast with random postures in reality. In this letter, we propose a hierarchical neural network, named Local Frame Network (LFNet), based on the local rotation invariant coordinate frame for robust point cloud analysis. The local point patches
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Fast Variational Bayesian Inference for Temporally Correlated Sparse Signal Recovery IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-05 Zheng Cao; Jisheng Dai; Weichao Xu; Chunqi Chang
The performance of sparse signal recovery (SSR) can be enhanced by exploiting rich temporal correlation in the multiple snapshots of signal of interest. However, existing methods need to transform the temporally correlated multiple measurements SSR problem into its vectorization form, imposing huge computational cost for algorithmic realization. To overcome this drawback, we propose a novel formulation
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Adaptive Spatiotemporal Graph Convolutional Networks for Motor Imagery Classification IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-08 Biao Sun; Han Zhang; Zexu Wu; Yunyan Zhang; Ting Li
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain computer interfaces (BCI). In view of the characteristics of non-stationarity, time-variability and individual diversity of EEG signals, a novel framework based on graph neural network is proposed for MI-EEG classification. First, an adaptive graph convolutional layer (AGCL) is constructed, by which the electrode
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Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-08 Julian Fritsch; Mathew Magimai-Doss
In the literature, the task of dysarthric speech intelligibility assessment has been approached through development of different low-level feature representations, subspace modeling, phone confidence estimation or measurement of automatic speech recognition system accuracy. This paper proposes a novel approach where the intelligibility is estimated as the percentage of correct words uttered by a speaker
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Finite-Length Bounds on Hypothesis Testing Subject to Vanishing Type I Error Restrictions IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-08 Sebastian Espinosa; Jorge F. Silva; Pablo Piantanida
A central problem in Binary Hypothesis Testing (BHT) is to determine the optimal tradeoff between the Type I error (referred to as false alarm ) and Type II (referred to as miss ) error. In this context, the exponential rate of convergence of the optimal miss error probability — as the sample size tends to infinity — given some (positive) restrictions on the false alarm probabilities is a fundamental
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EACNet: Enhanced Asymmetric Convolution for Real-Time Semantic Segmentation IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-15 Yaqian Li; Xiaokun Li; Cunjun Xiao; Haibin Li; Wenming Zhang
Although deep neural networks have made significant progress in semantic segmentation, speed and computational cost still can’t meet the strict requirements of real-world applications. In this paper, we present an enhanced asymmetric convolution network (EACNet) to seek a balance between accuracy and speed. Specifically, we design a pair of enhancing asymmetric convolution modules constructed by depth-wise
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Robust Kernel Correlation Based Bi-Channel Signal Detection With Correlated Non-Gaussian Noise IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-05 Huadong Lai; Weichao Xu
This letter proposes a robust detector based on kernel correlation (KC) for detecting the presence of a common random signal shared in two channels corrupted by correlated non-Gaussian impulsive noise. A bivariate Gaussian mixture (GM) distribution is employed to simulate the correlation and impulsive characteristic of the noise across two channels. The test statistic is constructed by the dot product
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Two-Stream Encoder GAN With Progressive Training for Co-Saliency Detection IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-08 Xiaoliang Qian; Xi Cheng; Gong Cheng; Xiwen Yao; Liying Jiang
The recent end-to-end co-saliency models have good performance, however, they cannot express the semantic consistency among a group of images well and usually require many co-saliency labels. To this end, a two-stream encoder generative adversarial network (TSE-GAN) with progressive training is proposed in this paper. In the pre-training stage, the salient object detection generative adversarial networks
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Face Image Inpainting With Evolutionary Generators IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-01 Chong Han; Junli Wang
Recently, deep learning has become a mainstream method of image inpainting. It can not only restore the image texture, obtain high-level abstract features of images, but also restore semantic images such as human face images. Among these methods, generative adversarial networks (GANs) using autoencoder as the generator have become the promising model for image inpainting. These models implement the
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An Integral-Based Approach to Orthogonal AMP IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-24 Yiyao Cheng; Lei Liu; Li Ping
Approximate message passing (AMP) is an iterative signal recovery algorithm for compressed sensing (CS) applications. In this letter, we present an integral-based orthogonal AMP (IB-OAMP) technique that avoids the requirements of AMP (and also the original form of OAMP) on differentiable and separable denoisers. The orthogonality in IB-OAMP can be established using a Monte Carlo method similar to the
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Attenuation Coefficient Guided Two-Stage Network for Underwater Image Restoration IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-31 Yufei Lin; Liquan Shen; Zhengyong Wang; Kun Wang; Xi Zhang
Underwater images suffer from severe color casts, low contrast and blurriness, which are caused by scattering and absorption when light propagates through water. However, existing deep learning methods treat the restoration process as a whole and do not fully consider the underwater physical distortion process. Thus, they cannot adequately tackle both absorption and scattering, leading to poor restoration
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Semantic Information Oriented No-Reference Video Quality Assessment IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-01 Wei Wu; Qinyao Li; Zhenzhong Chen; Shan Liu
In this letter, a method called Semantic Information Oriented No-Reference (SIONR) video quality assessment model is developed, which can effectively represent quality degradation of video by taking the variations of semantic information into consideration. Specially, temporal variations of the semantic features between adjacent frames are calculated to consider the inconsistency of the static semantic
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Contrast Enhancement of Multiple Tissues in MR Brain Images With Reversibility IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-05 Hao-Tian Wu; Kaihan Zheng; Qi Huang; Jiankun Hu
Contrast enhancement (CE) of magnetic resonance (MR) brain images is an important technique to bring out the tissue details for clinical diagnosis. Recently, a new form of image enhancement has been proposed to complete the task without any information loss. Specifically, information required to restore the original image is reversibly hidden into the enhanced image. Moreover, several image segmentation
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Data-Driven Parameter Choice for Illumination Artifact Correction of Digital Images IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-24 Hong-Phuong Dang; Myriam Vimond; Ségolen Geffray
We propose a new procedure for image illumination correction with data-driven parameter choice. This procedure aims at estimating the reflectance image from a corrupted version in which the corruption is due to pointwise multiplicative illumination artifact. The $\log \text{-illumination}$ artefact consists of “smooth” variations of the intensity which are modelled by a function lying in a finite dimensional
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Predicting Spatio-Temporal Entropic Differences for Robust No Reference Video Quality Assessment IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-06 Shankhanil Mitra; Rajiv Soundararajan; Sumohana S. Channappayya
We consider the problem of robust no reference (NR) video quality assessment (VQA) where the algorithms need to have good generalization performance when they are trained and tested on different datasets. We specifically address this question in the context of predicting video quality for compression and transmission applications. Motivated by the success of the spatio-temporal entropic differences
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Bayesian Post-Model-Selection Estimation IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-05 Nadav Harel; Tirza Routtenberg
Estimation after model selection refers to the problem where the exact observation model is unknown and is assumed to belong to a set of candidate models. Thus, a data-based model-selection stage is performed prior to the parameter estimation stage, which affects the performance of the subsequent estimation. In this letter, we investigate post-model-selection Bayesian parameter estimation of a random
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The Property of Frequency Shift in 2D-FRFT Domain With Application to Image Encryption IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2021-01-08 Lei Gao; Lin Qi; Ling Guan
The Fractional Fourier Transform (FRFT) has been playing a unique and increasingly important role in signal and image processing. In this letter, we investigate the property of frequency shift in two-dimensional FRFT (2D-FRFT) domain. It is shown that the magnitude of image reconstruction from phase information is frequency shift-invariant in 2D-FRFT domain, enhancing the robustness of image encryption
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Learning Unsupervised and Supervised Representations via General Covariance IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-10 Yun-Hao Yuan; Jin Li; Yun Li; Jianping Gou; Jipeng Qiang
Component analysis (CA) is a powerful technique for learning discriminative representations in various computer vision tasks. Typical CA methods are essentially based on the covariance matrix of training data. But, the covariance matrix has obvious disadvantages such as failing to model complex relationship among features and singularity in small sample size cases. In this letter, we propose a general
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A Novel $H_2$ Approach to FIR Prediction Under Disturbances and Measurement Errors IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-31 Jorge Ortega-Contreras; Eli Pale-Ramon; Yuriy S. Shmaliy; Yuan Xu
A novel approach is proposed to $H_2$ finite impulse response (FIR) prediction in discrete-time state-space. The biased-constrained $H_2$ optimal unbiased FIR ( $H_2$ -OUFIR) predictor derived under disturbances and measurement errors is shown to have the maximum likelihood form and be equivalent to the OUFIR predictor under Gaussian noise. The derivation is provided using the backward Euler method
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Compressed Super-Resolution of Positive Sources IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-16 Maxime Ferreira Da Costa; Yuejie Chi
Atomic norm minimization is a convex optimization framework to recover point sources from a subset of their low-pass observations, or equivalently the underlying frequencies of a spectrally-sparse signal. When the amplitudes of the sources are positive, a positive atomic norm can be formulated, and exact recovery can be ensured without imposing a separation between the sources, as long as the number
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Local Graph Clustering With Network Lasso IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-18 Alexander Jung; Yasmin SarcheshmehPour
We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundaries and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total variation of cluster indicator signals
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Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-14 HanQin Cai; Keaton Hamm; Longxiu Huang; Jiaqi Li; Tao Wang
Robust principal component analysis (RPCA) is a widely used tool for dimension reduction. In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR (IRCUR), for solving RPCA problems, which dramatically improves the computational efficiency in comparison with the existing algorithms. IRCUR achieves this acceleration by employing CUR decomposition when updating the low rank component
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Non-Autoregressive Transformer for Speech Recognition IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-14 Nanxin Chen; Shinji Watanabe; Jesús Villalba; Piotr Żelasko; Najim Dehak
Very deep transformers outperform conventional bi-directional long short-term memory networks for automatic speech recognition (ASR) by a significant margin. However, being autoregressive models, their computational complexity is still a prohibitive factor in their deployment into production systems. To amend this problem, we study two different non-autoregressive transformer structures for ASR: Audio-Conditional
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Total Variation Constrained Graph-Regularized Convex Non-Negative Matrix Factorization for Data Representation IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-25 Miao Tian; Chengcai Leng; Haonan Wu; Anup Basu
We propose a novel NMF algorithm, named Total Variation constrained Graph-regularized Convex Non-negative Matrix Factorization (TV-GCNMF), to incorporate total variation and graph Laplacian with convex NMF. In this model, the feature details of the data are preserved by a diffusion coefficient based on the gradient information. The graph regularization and convex constraints reveal the intrinsic geometry
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On the Identifiability of Sparse Vectors From Modulo Compressed Sensing Measurements IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-28 Dheeraj Prasanna; Chandrasekhar Sriram; Chandra R. Murthy
Compressed sensing deals with recovery of sparse signals from low dimensional projections, but under the assumption that the measurement setup has infinite dynamic range. In this letter, we consider a system with finite dynamic range, and to counter the clipping effect, the measurements crossing the range are folded back into the dynamic range of the system through modulo arithmetic. For this setup
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Graph-Theoretic Properties of Sub-Graph Entropy IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-28 Bhaskar Sen; Keshab K. Parhi
Sub-graph entropy has recently been applied to functional brain network analysis for identifying important brain regions associated with different brain states and for discriminating brain networks of subjects with psychiatric disorders from healthy controls. This letter describes two pertinent properties of sub-graph entropy. It is shown that when a graph is divided into multiple smaller graphs, the
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On a Particular Family of Differential Beamformers With Cardioid-Like and No-Null Patterns IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-12-28 Xudong Zhao; Jacob Benesty; Gongping Huang; Jingdong Chen
Differential microphone arrays (DMAs), which are responsive to the differential acoustic pressure fields, have been used in a wide range of applications related to audio and speech. The core part of a DMA is the so-called differential beamformer, which is generally designed by placing a number of nulls in its beampattern to attenuate noise from some directions. But the presence of these nulls may cause
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Robust Downlink Transmit Optimization Under Quantized Channel Feedback via the Strong Duality for QCQP IEEE Signal Process. Lett. (IF 3.105) Pub Date : 2020-11-16 Xianming Lin; Yongwei Huang; Wing-Kin Ma
Consider a robust multiple-input single-output downlink beamforming optimization problem in a frequency division duplexing system. The base station (BS) sends training signals to the users, and every user estimates the channel coefficients, quantizes the gain and the direction of the estimated channel and sends them back to the BS. Suppose that the channel state information at the transmitter is imperfectly
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