In this paper, we present a novel image denoising algorithm, specifically designed to effectively restore both the edges and texture of images. This is achieved through the use of an innovative model known as the overlapping group sparse fractional-o...
This paper presents an innovative method for designing finite impulse response (FIR) filters. The method optimizes the desired frequency response attributes while simultaneously increasing the sparsity of the filter coefficients. Traditional FIR filt...
Hyperspectral image (HSI) super-resolution is a vital technique that generates high spatial-resolution HSI (HR-HSI) by integrating information from low spatial-resolution HSI with high spatial-resolution multispectral image (MSI). However, existing s...
The sparsity regularization based on the L1 norm can significantly stabilize the solution of the ill-posed sparsity inversion problem, e.g., azimuth super-resolution of radar forward-looking imaging, which can effectively suppress the noise and reduc...
This paper focuses on image compressive sensing (CS). As the intrinsic properties of natural images, nonlocal self-similarity and sparse representation have been widely used in various image processing tasks. Most existing image CS methods apply eith...
The total variation (TV) regularization-based methods are proven to be effective in removing random noise. However, these solutions usually have staircase effects. This paper proposes a new image reconstruction method based on TV regularization with...
Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of...
Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to direc...
A hyperspectral image (HSI) contains many narrow spectral channels, thus containing efficient information in the spectral domain. However, high spectral resolution usually leads to lower spatial resolution as a result of the limitations of sensors. H...
A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the p...
Streak Tube Imaging Lidar (STIL) offers significant advantages in long-range sensing and ultrafast diagnostics by encoding spatial-temporal information as streaks, and hence decodes 3D images using tailored algorithm. However, under low-photon condit...
Side Scan Sonar (SSS) is widely used to search for seabed objects such as ships and wrecked aircraft due to its high-imaging-resolution and large planar scans. SSS requires an automatic real-time target recognition system to enhance search and rescue...
This paper considers the problem of how to efficiently measure a large and complex information field with optimally few observations. Specifically, we investigate how to stochastically estimate modular criticality values in a large-scale digital circ...
Multi-circular SAR(MCSAR) can obtain holographic three-dimensional (3D) images of interesting observation targets, which is a significant research field at present. For anisotropic scatterers, the multi-circular SAR incoherent 3D imaging strategy com...
Although Least Squares Support Vector Regression (LSSVR) reduces the hyperparameter space to two, it sacrifices sparsity, causing all training samples to become support vectors and increasing storage costs. In contrast, standard Support Vector Regres...
Recently, the broad learning system (BLS) has been widely developed in virtue of its excellent performance and high-computational efficiency. However, two deficiencies still exist in BLS and preclude its deployment in real applications. First, the st...
The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data s...
This review surveys the mathematical theory and methods of state estimation for stochastic dynamical systems when the Gaussian-linear-least-squares paradigm fails, focusing on the role of sparsity-promoting and constraint-encoding regularization. The...
Non-negative matrix factorization (NMF) is widely used as a powerful matrix factorization tool in data representation. However, the traditional NMF, measured by Euclidean distance or Kullback–Leibler distance, does not take into account the int...
Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of...
Fluid simulation can be automatically interpolated by using data-driven fluid simulations based on a space-time deformation. In this paper, we propose a novel data-driven fluid simulation scheme with the
Zero inflation is pervasive across text mining, event log, and sensor analytics, and it often degrades the predictive performance of analytical models. Classical approaches, most notably the zero-inflated Poisson (ZIP) and zero-inflated negative bino...
In this work, we proposed a sparse version of the Support Vector Regression (SVR) algorithm that uses regularization to achieve sparsity in function estimation. To achieve this, we used an adaptive L0 penalty that has a ridge structure and, therefore...
Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel. However, the sparse representation has not...
In this paper, a novel smooth group
Due to the complex background and low spatial resolution of the hyperspectral sensor, observed ground reflectance is often mixed at the pixel level. Hyperspectral unmixing (HU) is a hot-issue in the remote sensing area because it can decompose the ob...
Conversion from distributed acoustic sensing (DAS) measurements to geophone-equivalent data is important for integrating DAS into conventional seismic workflows. This is because most established seismic-processing algorithms are designed for particle...
Short-range imaging radar, with its all-day and all-weather perception capabilities, has gained considerable attention in emerging fields such as autonomous vehicle sensing and industrial robotic perception. However, compared to traditional imaging r...
In this paper, we propose a new hyperspectral image (HSI) denoising model with the group sparsity regularized hybrid spatio-spectral total variation (GHSSTV) and low-rank tensor decomposition, which is based on the analysis of structural sparsity of...
In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud an...
Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmissi...
Semi-Nonnegative Matrix Factorization (Semi-NMF), as a variant of NMF, inherits the merit of parts-based representation of NMF and possesses the ability to process mixed sign data, which has attracted extensive attention. However, standard Semi-NMF s...
As a dominant method for face recognition, the subspace learning algorithm shows desirable performance. Manifold learning can deal with the nonlinearity hidden in the data, and can project high dimensional data onto low dimensional data while preserv...
This paper presents a new l1-RLS method to estimate a sparse impulse response estimation. A new regularization factor calculation method is proposed for l1-RLS that requires no information of the true channel response in advance. In addition, we also...
Image set matching (ISM) has attracted increasing attention in the field of computer vision and pattern recognition. Some studies attempt to model query and gallery sets under a joint or collaborative representation framework, achieving impressive pe...
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment....
Inverse problems arise in many areas of science and engineering, such as geophysics, biology, and medical imaging. One of the main imaging modalities that have seen a huge increase in recent years is the noninvasive, nonionizing, and radiation-free i...
Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include mult...
Hyperspectral image (HSIs) denoising is a preprocessing step that plays a crucial role in many applications used in Earth observation missions. Low-rank tensor representation can be utilized to restore mixed-noise HSIs, such as those affected by mixe...
In this paper, we propose a group Lasso regularization term as a hidden layer regularization method for feedforward neural networks. Adding a group Lasso regularization term into the standard error function as a hidden layer regularization term is a...
Combining multiple data sources, Digital Earth is an integrated observation platform based on air–space–ground–sea monitoring systems. Among these data sources, the Inverse Synthetic Aperture Radar (ISAR) is a crucial observation me...
Based on the Support Vector Machine (SVM) and Twin Parametric Margin SVM (TPMSVM), this paper proposes two sparse models, named Sparse SVM (SSVM) and Sparse TPMSVM (STPMSVM). The study aims to achieve high sparsity, rapid prediction, and strong gener...
As bandwidth-intensive applications proliferate and the usage of wireless devices surges, fifth-generation (5G) and beyond (B5G) networks are challenged to enhance coverage, reduce latency, and improve efficiency. The application of machine learning...
Vibration monitoring is one of the most effective approaches for bearing fault diagnosis. Within this category of techniques, sparsity constraint-based regularization has received considerable attention for its capability to accurately extract repeti...
In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One...
In recent years, sparsity-driven regularization and compressed sensing (CS)-based radar imaging methods have attracted significant attention. This paper provides an introduction to the fundamental concepts of this area. In addition, we will describe...
The utilization of the inversion-based algorithm for spectral decomposition using constrained least-squares spectral analysis (CLSSA) facilitates a time–frequency spectrum with higher temporal and frequency resolution. The conventional CLSSA al...
Conventional total variation (TV) regularization methods based on Laplacian or fixed-scale Hyper-Laplacian priors impose uniform sparsity penalties on gradients. These uniform penalties fail to capture the heterogeneous sparsity characteristics acros...
For the problem where the existing hyperspectral unmixing methods do not take full advantage of the correlations and differences between all these bands, resulting in affecting the final unmixing results, we design an enhanced 2DTV (E-2DTV) regulariz...
This paper proposes a new group-sparsity-inducing regularizer to approximate
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