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Variable Kernel Width Algorithm of Generalized Maximum Correntropy Criteria for Censored Regression

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1877-1881页

作者： Zhao, Haiquan Chen, Bing Zhu, Yingying He, Xiaoqiong Shu, Zeliang Southwest Jiaotong Univ Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The constant kernel width of generalized maximum correntropy criteria (GMCC) has arisen that the steady-state error and convergence speed can be mutually exclusive. To solve this problem, this brief proposes the variable kernel width (VKW) GMCC algorithm. Actually, due to the censored problem, the output data value beyond the limit of the recording device can not be well observed. In this case, we further developed a variable kernel width GMCC algorithm based on censored regression (CR-VKWGMCC). Simulation results show that the proposed CR-VKWGMCC algorithm has excellent performance in both Gaussian and non Gaussian noise.

关键词： signal processing algorithms Kernel Steady-state Filtering algorithms Convergence Adaptive filters Optimized production technology Generalized maximum correntropy criteria variable kernel width censored regression adaptive filtering

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Bias Adjusted Sign Covariance Matrix

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IEEE signal processing LETTERS 2022年 29卷 339-343页

作者： Raninen, Elias Ollila, Esa Aalto Univ Dept Signal Proc & Acoust FI-00076 Aalto Finland

The spatial sign covariance matrix (SSCM), also known as the normalized sample covariance matrix (NSCM), has been widely used in signal processing as a robust alternative to the sample covariance matrix (SCM). It is well-known that the SSCM does not provide consistent estimates of the eigenvalues of the shape matrix (normalized scatter matrix). To alleviate this problem, we propose BASIC (Bias Adjusted SIgn Covariance), which performs an approximate bias correction to the eigenvalues of the SSCM under the assumption that the samples are generated from zero mean unspecified complex elliptically symmetric distributions (the real-valued case is also addressed). We then use the bias correction in order to develop a robust regularized SSCM based estimator, BASIC Shrinkage estimator (BASICS), which is suitable for high dimensional problems, where the dimension can be larger than the sample size. We assess the proposed estimator with several numerical examples as well as in a linear discriminant analysis (LDA) classification problem with real data sets. The simulations show that the proposed estimator compares well to competing robust covariance matrix estimators but has the advantage of being significantly faster to compute.

关键词： Covariance matrices Eigenvalues and eigenfunctions Shape Sensors Indexes Electric breakdown signal processing algorithms Covariance matrix elliptical distribution spatial sign covariance matrix robust estimation shrinkage

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Total Least Squares Normalized Subband Adaptive Filter Algorithm for Noisy Input

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1977-1981页

作者： Zhao, Haiquan Chen, Yida Liu, Jun Zhu, Yingying Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Chengdu Jiaoda Guangmang Technol Co Ltd Chengdu 610041 Peoples R China

Subband adaptive filter has been widely applied to process correlated input signals due to its decorrelation property. However, the performance of the subband adaptive filter algorithm will be drastically degraded in the case of both the input and output signals are contaminated with noise. To tackle this problem, this brief proposes a total least squares normalized subband adaptive filter (TLS-NSAF) algorithm,which is different from bias-compensated schemes. The proposed algorithm is derived by employing the Rayleigh quotient as the cost function and the gradient steepest descent method. The local mean stability and computational complexity of the proposed algorithm are also analyzed. Simulation results demonstrate that the TLS-NSAF algorithm has better performance in comparison with previous algorithms.

关键词： signal processing algorithms Convergence Filtering algorithms Adaptive filters Adaptation models Computational modeling Computational complexity Correlated input errors-in-variables model subband adaptive filter total least squares

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A Reversible Jump MCMC in Bayesian Blind Deconvolution With a Spherical Prior

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IEEE signal processing LETTERS 2022年 29卷 2372-2376页

作者： Traulle, Benjamin Bidon, Stephanie Roque, Damien Univ Toulouse ISAE SUPAERO F-31055 Toulouse France

Blind deconvolution (BD) is the recovery of a scene of interest convolved with an unknown impulse response function. Within a Bayesian context, prior distributions are assigned to both unknowns to regularize the BD problem. A common approach is to use Gaussian priors. Nonetheless, the latter do not alleviate the so-called scale ambiguity that prevents from efficiently sampling the joint posterior distribution and building appropriate estimators. In this paper, we instead use a Von-Mises prior that removes scale ambiguity and we focus on the design of a sampling scheme of the joint posterior. The latter may exhibit multiple modes. We propose accordingly a reversible jump Markov chain Monte Carlo method that prevents samples from lingering in local modes. Compared to state-of-the-art techniques, the algorithm shows an improved within- and between-mode mixing property with synthetic data. This paves the way for the design of Bayesian estimators naturally deprived of scale ambiguity.

关键词： Bayes methods signal processing algorithms Deconvolution Space exploration Monte Carlo methods Markov processes Convolution Blind deconvolution multimodal posteriors RJMCMC algorithm scale ambiguity von Mises-Fisher prior

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A Display-Independent Quality Assessment for HDR Images

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IEEE signal processing LETTERS 2022年 29卷 464-468页

作者： Zhang, Keke Fang, Ying Chen, Weiling Xu, Yiwen Zhao, Tiesong Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transm Fuzhou 350108 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

High Dynamic Range (HDR) images improve visual quality by representing a wide range of luminance in real world. HDR Image Quality Assessment (IQA) is essential in HDR processing technologies. This work focus on the IQA for the HDR applications represented by HDR compression. Towards designing IQA for these tasks, user-end display information should not be considered, given that these tasks only focus on the media intrinsic features. However, most existing HDR metrics are associated with display luminance, hence they can not handle IQA during HDR processing. In this work, we find that it is possible to evaluate HDR image quality without prior knowledge of luminance for display. Based on this, we proposed a low complexity Display-Independent Gradient Magnitude Similarity (DIGMS) metric. Experimental results demonstrate that the proposed DIGMS outperforms the state-of-the-arts in HDR IQA. This work opens up a new perspective for designing IQA algorithms for HDR images.

关键词： Image quality Image coding Task analysis Visualization signal processing algorithms Optimized production technology Complexity theory Image quality assessment (IQA) High Dynamic Range (HDR) gradient magnitude similarity (GMS)

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Speech Time-Scale Modification With GANs

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IEEE signal processing LETTERS 2022年 29卷 1067-1071页

作者： Cohen, Eyal Kreuk, Felix Keshet, Joseph Bar Ilan Univ Dept Comp Sci IL-5290002 Ramat Gan Israel

While listening to spoken content, it is often desired to vary the speech rate while preserving the speaker's timbre and pitch. To date, advanced signal processing techniques are used to address this task, but it still remains a challenge to maintain a high speech quality at all time-scales. Inspired by the success of speech generation using Generative Adversarial Networks (GANs), we propose a novel unsupervised learning algorithm for time-scale modification (TSM) of speech, called ScalerGAN. The model is trained using a set of speech utterances, where no time-scales are provided. The ScalerGAN algorithm is composed of a generator that gets as input speech with the desired rate and outputs a time-adjusted speech;a discriminator that works on various spectrum scales;and a decoder that converts the time-adjusted signal back to the original rate to maintain consistency. Using an A/B test and conditional A/B test, human listeners were asked to compare ScalerGAN with other state-of-the-art TSM methods. The results showed that the speech quality of ScalerGAN outperforms all other methods.

关键词： Spectrogram Generators signal processing algorithms Decoding Training Vocoders Time-domain analysis Deep neural networks generative adversarial networks speech synthesis time-scale modification

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Average Performance of OMP and Thresholding Under Dictionary Mismatch

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IEEE signal processing LETTERS 2022年 29卷 1077-1081页

作者： Pali, Marie-Christine Ruetz, Simon Schnass, Karin Univ Innsbruck Dept Math A-6020 Innsbruck Austria

This paper studies the performance of OMP in comparison to Thresholding in the case in which only a perturbed version of the generating dictionary is known. Both theory and numerical simulations show that simple Thresholding is a viable alternative to OMP in applications where only a perturbed version of the generating dictionary is given, such as dictionary learning.

关键词： Dictionaries Matching pursuit algorithms signal processing algorithms Perturbation methods Machine learning Atomic measurements Sparse representation Sparse approximation orthogonal matching pursuit perturbed dictionary average case analysis dictionary learning thresholding

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Multispectral Image Demosaicking Based on Novel Spectrally Localized Average Images

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IEEE signal processing LETTERS 2022年 29卷 449-453页

作者： Rathi, Vishwas Goyal, Puneet Indian Inst Technol Ropar Dept Comp Sci & Engn Rupnagar 140001 India

In thisletter, we propose a new generic multispectral image demosaicking algorithm using adaptive spectral correlation. Our proposed algorithm defines the spectrally localized average image (SLAI) corresponding to each spectral band, which has a strong spectral correlation with the pixel values of the corresponding spectral band in the raw image captured using a single sensor. The proposed algorithm uses the SLAI to estimate the missing pixel values of the spectral bands. Experimental results reveal that our algorithm outperforms other state-of-the-art generic multispectral image demosaicking algorithms in terms of objective and subjective evaluations.

关键词： signal processing algorithms Correlation Filtering algorithms Convolution Indexes Binary trees Hyperspectral imaging Image demosaicking multispectral filter array convolution filter interpolation

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Variational Bayesian and Generalized Approximate Message Passing-Based Sparse Bayesian Learning Model for Image Reconstruction

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IEEE signal processing LETTERS 2022年 29卷 2328-2332页

作者： Dong, Jingyi Lyu, Wentao Zhou, Di Xu, Weiqiang Zhejiang Sci Tech Univ Key Lab Intelligent Text & Flexible Interconnect Z Hangzhou 310018 Peoples R China Zhejiang UniView Technol Co Ltd Hangzhou Zhejiang Peoples R China

In this paper, we present a novel sparse Bayesian learning (SBL) framework for large-scale image recovery. We formulate variational Bayesian (VB) and generalized approximate message passing (GAMP) into the SBL model (called VGAMP-SBL) to speed up image reconstruction. GAMP can be argued a scalar estimation function described by a set of simple state evolution (SE) equations. From the SE equations, one can accurately predict the values of SBL Params, while it can obtain better reconstruction results without matrix inversion. Moreover, the interaction between data fluctuations and parameter fluctuations is negligible in VB structure, so the maximum marginal likelihood function can be easily obtained, This improves the computation efficiency of our algorithm greatly. Experimental results corroborate these claims.

关键词： signal processing algorithms Image reconstruction Approximation algorithms Sparse Bayesian learning variational Bayesian generalized approximate message passing image reconstruction

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A Sharp Analysis of Covariate Adjusted Precision Matrix Estimation via Alternating Projected Gradient Descent

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IEEE signal processing LETTERS 2022年 29卷 877-881页

作者： Lv, Xiao Cui, Wei Liu, Yulong Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Phys Beijing 100081 Peoples R China

In this letter, we present a sharp algorithmic analysis for alternating projected gradient descent which is used to solve the covariate adjusted precision matrix estimation problem in high-dimensional settings. By introducing a new analytical tool (the generic chaining), we remove the impractical resampling assumption used in the literature. The new analysis also demonstrates that this algorithm not only enjoys a linear convergence rate in the absence of convexity, but also attains the minimax rate with optimal order of sample complexity. Our results, meanwhile, reveal a time-data tradeoff in this problem. Numerical experiments are provided to verify our theoretical results.

关键词： signal processing algorithms Convergence Estimation Complexity theory Estimation error Linear regression Prediction algorithms Multivariate linear regression covariate adjusted precision matrix estimation alternating gradient descent

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