Remote sensing images(RSIs)encompass abundant spatial and spec-tral/temporal information,finding wide applications in various ***,during image acquisition and transmission,RSI often encounter noise interference,which ...
详细信息
Remote sensing images(RSIs)encompass abundant spatial and spec-tral/temporal information,finding wide applications in various ***,during image acquisition and transmission,RSI often encounter noise interference,which adversely affects the accuracy of subsequent *** address this is-sue,this paper proposes a novel non-local fully connected tensor network(NLFCTN)decomposition algorithm for denoising RSI,aiming to fully exploit their global cor-relation and non-local self-similarity(NSS)***,as a recently de-veloped tensor decomposition technique,exhibits remarkable capability in captur-ing global correlations and minimizing information *** addition,we introduce an efficient algorithm based on proximal alternating minimization(PAM)to effi-ciently solve the model and prove the *** effectiveness of the pro-posed method is validated through denoising experiments on both simulated and real RSI data,employing objective evaluation metrics and subjective visual *** results of the experiment show that the proposed method outperforms other RSI denoising techniques in terms of denoising performance.
Variable selection for varying coefficient models includes the separation of varying and constant effects,and the selection of variables with nonzero varying effects and those with nonzero constant *** paper proposes ...
详细信息
Variable selection for varying coefficient models includes the separation of varying and constant effects,and the selection of variables with nonzero varying effects and those with nonzero constant *** paper proposes a unified variable selection approach called the double-penalized quadratic inference functions method for varying coefficient models of longitudinal *** proposed method can not only separate varying coefficients and constant coefficients,but also estimate and select the nonzero varying coefficients and nonzero constant *** is suitable for variable selection of linear models,varying coefficient models,and partial linear varying coefficient *** regularity conditions,the proposed method is consistent in both separation and selection of varying coefficients and constant *** obtained estimators of varying coefficients possess the optimal convergence rate of non-parametric function estimation,and the estimators of nonzero constant coefficients are consistent and asymptotically ***,the authors investigate the finite sample performance of the proposed method through simulation studies and a real data *** results show that the proposed method performs better than the existing competitor.
In this paper,we study the controllability of compressible Navier-Stokes equations with density dependent *** when the shear viscosityμis a positive constant and the bulk viscosityλis a function of the density,it is...
详细信息
In this paper,we study the controllability of compressible Navier-Stokes equations with density dependent *** when the shear viscosityμis a positive constant and the bulk viscosityλis a function of the density,it is proven that the system is exactly locally controllable to a constant target trajectory by using boundary control functions.
Traditional Nyquist rate sampling faces significant challenges as communication technology progresses towards 6G. Multi-coset sampling emerges as a viable solution by reducing the sampling rate. However, this method n...
详细信息
In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research *** assigning different weights to singular values,the weighted nuclear norm minimization(...
详细信息
In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research *** assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many ***,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise *** this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from *** apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this *** exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse *** results show that our method can effectively remove impulse noise.
Underwater image enhancement, as an important branch of image processing, has attracted the attention of many scholars in recent years. Due to selective scattering and degradation of light in water, images captured un...
详细信息
Most of the research on uncertainty and imprecision that is widespread in the real world can be abstracted into interval-valued optimization problems whose cost function or constraint function is a closed interval. In...
详细信息
Phishing URL detection has become a critical challenge in cybersecurity, with existing methods often struggling to maintain high accuracy while generalizing across diverse datasets. In this paper, we introduce BERT-Ph...
详细信息
A new scenario is considered that device-to-device (D2D) communication users underlay the spectrum resource of cellular user in distributed antenna systems (DAS) is discussed in this paper. We mainly focus on how to i...
详细信息
Low-dose computed tomography(LDCT)contains the mixed noise of Poisson and Gaus-sian,which makes the image reconstruction a challenging *** order to describe the statistical characteristics of the mixed noise,we adopt ...
详细信息
Low-dose computed tomography(LDCT)contains the mixed noise of Poisson and Gaus-sian,which makes the image reconstruction a challenging *** order to describe the statistical characteristics of the mixed noise,we adopt the sinogram preprocessing as a stan-dard maximum a posteriori(MAP).Based on the fact that the sinogram of LDCT has non-local self-similarity property,it exhibits low-rank *** conventional way of solving the low-rank problem is implemented in matrix forms,and ignores the correlations among similar patch *** avoid this issue,we make use of a nonlocal Kronecker-Basis-Representation(KBR)method to depict the low-rank problem.A new denoising model,which consists of the sinogram preprocessing for data fidelity and the nonlocal KBR term,is developed in this *** proposed denoising model can better illustrate the generative mechanism of the mixed noise and the prior knowledge of the ***-merical results show that the proposed denoising model outperforms the state-of-the-art algorithms in terms of peak-signal-to-noise ratio(PSNR),feature similarity(FSIM),and normalized mean square error(NMSE).
暂无评论