Int. J. Wavelets Multiresolution Inf. Process.最新文献_第10页

A note on phase (norm) retrievable real Hilbert space fusion frames 关于相位(范数)可检索的实希尔伯特空间融合坐标系的注记

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-20 DOI: 10.1142/s0219691322500400

P. Casazza, F. Akrami, A. Rahimi

引用次数: 4

Frame multiresolution analysis of continuous piecewise linear functions 连续分段线性函数的帧多分辨率分析

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-10 DOI: 10.1142/s0219691321500326

Siva Prasad Murugan, G. P. Youvaraj

{"title":"Frame multiresolution analysis of continuous piecewise linear functions","authors":"Siva Prasad Murugan, G. P. Youvaraj","doi":"10.1142/s0219691321500326","DOIUrl":"https://doi.org/10.1142/s0219691321500326","url":null,"abstract":"The Franklin wavelet is constructed using the multiresolution analysis (MRA) generated from a scaling function [Formula: see text] that is continuous on [Formula: see text], linear on [Formula: see text] and [Formula: see text] for every [Formula: see text]. For [Formula: see text] and [Formula: see text], it is shown that if a function [Formula: see text] is continuous on [Formula: see text], linear on [Formula: see text] and [Formula: see text], for [Formula: see text], and generates MRA with dilation factor [Formula: see text], then [Formula: see text]. Conversely, for [Formula: see text], it is shown that there exists a [Formula: see text], as satisfying the above conditions, that generates MRA with dilation factor [Formula: see text]. The frame MRA (FMRA) is useful in signal processing, since the perfect reconstruction filter banks associated with FMRA can be narrow-band. So it is natural to ask, whether the above results can be extended for the case of FMRA. In this paper, for [Formula: see text], we prove that if [Formula: see text] generates FMRA with dilation factor [Formula: see text], then [Formula: see text]. For [Formula: see text], we prove similar results when [Formula: see text]. In addition, for [Formula: see text] we prove that there exists a function [Formula: see text] as satisfying the above conditions, that generates FMRA. Also, we construct tight wavelet frame and wavelet frame for such scaling functions.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116380706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wavelet estimation for factor models with time-varying loadings 时变负荷因子模型的小波估计

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-08 DOI: 10.1142/S0219691321500338

Duván Humberto Cataño, C. Rodríguez-Caballero, D. Peña, Chang Chiann

引用次数: 1

Sparse phase retrieval via ℓp (0 p ≤ 1) minimization 稀疏相位检索通过最小化l_p (0 p≤1)

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-05 DOI: 10.1142/S021969132150034X

Manxia Cao, Wei Huang

引用次数: 0

Partial Gabor frames and dual frames 部分Gabor框架和双框架

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-05 DOI: 10.1142/S0219691321500351

Yu Tian, Hui-Fang Jia, Guo-Liang He

引用次数: 1

Wavelet packets associated with linear canonical transform on spectrum 与谱上线性正则变换相关的小波包

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-01 DOI: 10.1142/S0219691321500302

M. Y. Bhat, Aamir Hamid Dar

引用次数: 9

Design of nonlinear optimal tracking control law based on finite-time stability for quadratic performance index 基于有限时间稳定性的二次型性能指标非线性最优跟踪控制律设计

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-06-26 DOI: 10.1142/s0219691321500314

Di Wang, Can Liu, Suixiang Gao, Fu-cheng Liao

引用次数: 0

Frame of translates and its canonical dual in L2(G) 翻译框架及其L2(G)规范对偶

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-06-19 DOI: 10.1142/S0219691321500296

Majid Jabbar Saadan, M. Janfada, R. A. Kamyabi-Gol

引用次数: 0

Noise reduction of shot-noise-dominated hyperspectral imagery by combining PCA with existing denoising methods 结合PCA和现有降噪方法对高光谱图像进行降噪

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-06-19 DOI: 10.1142/s0219691321500284

Guangyi Chen, A. Krzyżak

{"title":"Noise reduction of shot-noise-dominated hyperspectral imagery by combining PCA with existing denoising methods","authors":"Guangyi Chen, A. Krzyżak","doi":"10.1142/s0219691321500284","DOIUrl":"https://doi.org/10.1142/s0219691321500284","url":null,"abstract":"In this paper, we revisit the effects of principal component analysis (PCA) on hyperspectral imagery denoising. Our previous work combined PCA with wavelet shrinkage and particularly good denoising results has been achieved. We debate that any denoising methods can be used to replace wavelet shrinkage in our PCA+wavelet shrinkage algorithm. The major difference between this work and our previous PCA-based denoising method is that we consider a mixture of Gaussian and shot noise in this work whereas our previous methods studied Gaussian white noise alone. In addition, we retain [Formula: see text] [Formula: see text] PCA output components in our forward PCA transform in this paper whereas we keep all PCA output components [Formula: see text] in our previous works. The [Formula: see text] above is the number of spectral bands in the original hyperspectral imagery data cube. In addition, PCA is much better than nonlinear PCA for hyperspectral imagery denoising when Gaussian white noise and shot noise are introduced as demonstrated in this paper. Extensive experiments demonstrate that the method proposed in this paper outperforms the existing methods significantly in terms of signal-to-noise ratio for two testing hyperspectral imagery data cubes.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129609711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-view feature selection via sparse tensor regression 稀疏张量回归的多视图特征选择

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-05-17 DOI: 10.1142/S021969132150020X

Haoliang Yuan, Sio-Long Lo, Ming Yin, Yong Liang

引用次数: 1