检索结果-内蒙古大学图书馆

Stability Analysis of l0,∞-Norm Based convolutional sparse coding Using Stripe Coherence

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 5810-5823页

作者： Fu, Yuli Zhu, Tao Xiang, Youjun Chen, Zhen Cai, Lei South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China

Theoretical guarantees for the l(0,infinity)-pseudo-norm based convolutional sparse coding have been established in a recent work. However, the stability analysis in the noisy case via the stripe coherence is absent. This coherence is a stronger characterization of the convolutional dictionary, and a considerably more informative measure than the standard global mutual coherence. The present paper supplements this missing part. Formally, three main results together with their proofs are given. The first one is for the stability of the solution to the P-0,infinity(epsilon) problem, the second one and the third one are for the stable recovery of orthogonal matching pursuit (OMP) algorithm in the presence of noise. Under a reasonable assumption, the first two results are compared with the corresponding two that use the global mutual coherence in the previous work, respectively, showing the advantages of our results: 1) the stability guarantee conditions are at least as strong as the existing ones;2) the upper-bound of the distance between the true sparse vector and the solution to the P-0,infinity(epsilon) problem is tighter than the counterpart, so is the one between the true sparse vector and the solution obtained byOMP. Moreover, the second result is compared with the third one, stressing the difference in the restriction on noise energy. Also, some experiments are presented to intuitively compare the aforesaid upper-bounds.

关键词： Stability analysis l(0,infinity)-norm convolutional sparse coding stripe coherence

来源：评论

学校读者我要写书评

暂无评论

Phase-Based Palmprint Identification With convolutional sparse coding

IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIE...

引用

IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIENCE 2022年第3期4卷 424-438页

作者： Marval-Perez, Luis Rafael Ito, Koichi Aoki, Takafumi Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan LIONS Data Inc Tokyo 1060032 Japan

Phase-based image matching has shown high recognition accuracy in palmprint verification. The algorithm compares a pair of palmprint images by extracting local phase features from the images and computing local correlation functions between them. A major drawback of this algorithm is its high computational cost associated with the evaluation of local correlation functions. This needs to be addressed, especially in the case of one-to-many comparisons required for palmprint identification. The problem becomes increasingly severe as the number of enrolled images increases. In this paper, we propose a novel palmprint identification algorithm with low computational complexity, which employs a sparse representation of enrolled phase features (i.e., phase templates) to evaluate local correlation functions. For this purpose, we also develop an efficient convolutional sparse coding (CSC) algorithm that can derive a compact representation of phase templates. The proposed method reduces the computational cost of phase-based palmprint identification without significant degradation of recognition performance. Our experiments using public databases clearly demonstrate the advantage of the proposed method over conventional methods.

关键词： convolutional sparse coding palmprint identification phase-based image matching phase correlation phase features biometrics

来源：评论

学校读者我要写书评

暂无评论

Pansharpening of Satellite Images with convolutional sparse coding and Adaptive PCNN-Based Approach

引用

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2021年第12期49卷 2989-3004页

作者： Sangani, Dhara J. Thakker, Rajesh A. Panchal, S. D. Gogineni, Rajesh Vishwakarma Govt Engn Coll Chandkheda Elect & Commun Dept Gandhi Sagar 382424 Gujarat India Govt Engn Coll Dept ECE Rajkot Gujarat India Gujarat Technol Univ Comp Engn Ahmadabad Gujarat India Dhanekula Inst Engn & Technol Dept Elect & Commun Engn Vijayawada 521139 India

In remote sensing, Pansharpening process has great significance in many practical applications like map updating, hazard monitoring, target recognition and object classification. Satellite sensors capturing panchromatic and multispectral images with complementary characteristics due to tradeoff between IFOV (instantaneous field of view) and SNR (signal-to-noise ratio). Pansharpening is a process of combining PAN (panchromatic) image of high spatial resolution with MS (multispectral) image of high spectral resolution to get image of high spectral and spatial resolution. In Pansharpening, balancing between extraction of information and injection of information is crucial point;misbalancing can cause intensity distortion. Proposed method is a combination of CSC (convolution sparse coding) and adaptive PCNN (pulse coupled neural network) approach. NSST (non-sub-sampled shearlet transform) is used for band separation of PAN and MS image. CSC is used for fusing low pass sub-bands, and adaptive PCNN method is employed for fusing high pass sub-bands. Five datasets with different geographical areas like mountain, urban and vegetation area are used for experiment purpose. Visual results and quantitative index analysis reflect the superiority of proposed method in preserving spectral details in pansharpened image.

关键词： Pansharpening Non-subsampled shearlet transform convolutional sparse coding Adaptive pulse couple neural network High-resolution multispectral image

来源：评论

学校读者我要写书评

暂无评论

Efficient ADMM-Based Algorithms for convolutional sparse coding

引用

IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 389-393页

作者： Veshki, Farshad Vorobyov, Sergiy Aalto Univ Dept Signal Proc & Acoust Espoo 02150 Finland

convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model. The most efficient convolutional sparse coding methods are based on the alternating direction method of multipliers and the convolution theorem. The only major difference between these methods is how they approach a convolutional least-squares fitting subproblem. In this letter, we present a novel solution for this subproblem, which improves the computational efficiency of the existing algorithms. The same approach is also used to develop an efficient dictionary learning method. In addition, we propose a novel algorithm for convolutional sparse coding with a constraint on the approximation error. Source codes for the proposed algorithms are available online.

关键词： Convolution Signal processing algorithms Dictionaries convolutional codes Approximation algorithms Optimization Image coding convolutional sparse coding constrained sparse approximation dictionary learning alternating direction method of multipliers

来源：评论

学校读者我要写书评

暂无评论

Time-Domain Wideband DOA Estimation Under the convolutional sparse coding Framework

引用

IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 274-278页

作者： Wan, Zhengyu Liu, Wei Univ Sheffield Dept Elect & Elect Engn Commun Res Grp Sheffield S1 3JD S Yorkshire England

The wideband direction of arrival (DOA) estimation problem can be formulated into a narrowband form by applying discrete Fourier Transform (DFT) to sensor measurements;however, a large number of temporal snapshots are required in order to meet the narrowband assumption in the frequency-domain. To reduce the number of snapshots required, a convolutional sparse coding (CSC) based wideband signal model is proposed for direct time-domain DOA estimation, and a group sparsity based minimization problem is formulated. Simulation results indicate that the proposed time-domain CSC (TD-CSC) based method has a better performance than the frequency-domain method, but with a higher computational complexity.

关键词： Direction-of-arrival estimation Wideband Estimation Time-domain analysis Discrete Fourier transforms Convolution Narrowband Wideband array DOA estimation convolutional sparse coding time domain

来源：评论

学校读者我要写书评

暂无评论

convolutional sparse coding with periodic overlapped group sparsity for rolling element bearing fault diagnosis

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2018年第11期29卷 115103-115103页

作者： Xia, Yi Lu, Siliang Univ Anhui Dept Elect Engn & Automat Hefei 230601 Anhui Peoples R China

To cope with the problem of detecting periodic impulses in rotating machines with certain bearing faults, this paper proposes a novel data-driven dictionary learning and sparse coding algorithm. In our approach, the signal is decomposed into one or several components with each one as a convolution of one atom with a sparse activation vector. This manner of signal decomposition is formulated as a convex problem subject to a constraint that the activation vector should have a structure with periodic group sparsity. The solution to such a problem is an alternative optimizing process, that is, with a given dictionary, a sparse coding problem is solved by a split variable augmented Lagrangian shrinkage algorithm (SALSA), and for a fixed activation vector, the dictionary updating is implemented by solving Lagrange dual problem. The advantages of the proposed model over several other approaches are demonstrated by the experiments on both simulated and real vibration signals. The experimental results indicate that the proposed method can effectively detect and extract the latent weak fault impulses even in the presence of heavy noise

关键词： fault diagnosis convolutional sparse coding dictionary learning group sparsity

来源：评论

学校读者我要写书评

暂无评论

convolutional sparse coding framework for compressive spectral imaging

引用

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2020年 66卷 102690-102690页

作者： Barajas-Solano, Crisostomo Ramirez, Juan-Marcos Arguello, Henry Univ Ind Santander Dept Elect Engn Bucaramanga Santander Colombia Univ Ind Santander Dept Comp Sci Bucaramanga Santander Colombia

Spectral images (SI) can be represented as 3D-arrays of spatial information across multiple wavelengths. Compressive Spectral Imaging (CSI) reduces sensing costs by sensing compressed versions of the scene, recovering a suitable version of the original SI solving a sparsity-inducing inverse problem. On the other hand, convolutional sparse coding (CSC) has been successfully proved for representing gray-scale images, however it misses any correlation between images. This work considers the spatial-spectral correlation within SIs introducing an extension of the CSC signal model describing the SI as the sum of convolutions of 3D sparse coefficient maps with their respective 3D dictionary filters. Furthermore, we use the proposed CSC framework for recovering SIs from CSI measurements. The simulations results, using two different CSI acquisition architectures, show that the proposed CSC framework yields better representations of the SIs than those obtained under the traditional sparse signal representation approach, improving the quality of the recovered Sls. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Compressive spectral imaging convolutional sparse coding sparse representation Spectral images

来源：评论

学校读者我要写书评

暂无评论

A Novel Fusion Strategy and convolutional sparse coding for Robot Multisource Image Fusion

引用

AUTOMATIC CONTROL AND COMPUTER SCIENCES 2023年第2期57卷 185-195页

作者： Liu, Jintao Lu, Yigui Zhengzhou Univ Sci & Technol Sch Mech Engn Zhengzhou Peoples R China

Image fusion refers to the fusion of images collected by multiple sensors about the same target or scene into one image by image processing technology. In this way, the advantages of multiple sensors can be effectively utilized to obtain more comprehensive feature information of the target or scene, which is conducive to human eye observation and subsequent recognition and processing. The traditional fusion methods are easy to lose the image details, resulting in poor fusion effect. Therefore, this paper proposes a multisource image fusion method based on convolutional sparse coding and a novel fusion strategy. Firstly, the image is decomposed into low-rank and sparse through low-rank decomposition. Then, the sparse part is convoluted and decomposed to obtain a set of sparse filter dictionaries, which are applied to image fusion by convolutional sparse coding. The regional energy-Cauchy fuzzy function rule is adopted for low-rank components. The regional Laplace energy is used for sparse component. Finally, the weighted average method is used to obtain the final fusion result. Experimental results show that the proposed method achieves good results in terms of visual effects and objective evaluation indexes.

关键词： multisource image fusion convolutional sparse coding regional energy-Cauchy fuzzy function regional Laplace energy

来源：评论

学校读者我要写书评

暂无评论

convolutional sparse coding for Time Series Via a l0 Penalty: An Efficient Algorithm With Statistical Guarantees

引用

STATISTICAL ANALYSIS AND DATA MINING 2024年第6期17卷

作者： Truong, Charles Moreau, Thomas Univ Paris Saclay Ctr Borelli ENS Paris Saclay CNRS Gif Sur Yvette France Univ Paris Saclay INRIA CEA Palaiseau France

Identifying characteristic patterns in time series, such as heartbeats or brain responses to a stimulus, is critical to understanding the physical or physiological phenomena monitored with sensors. convolutional sparse coding (CSC) methods, which aim to approximate signals by a sparse combination of short signal templates (also called atoms), are well-suited for this task. However, enforcing sparsity leads to non-convex and untractable optimization problems. This article proposes finding the optimal solution to the original and non-convex CSC problem when the atoms do not overlap. Specifically, we show that the reconstruction error satisfies a simple recursive relationship in this setting, which leads to an efficient detection algorithm. We prove that our method correctly estimates the number of patterns and their localization, up to a detection margin that depends on a certain measure of the signal-to-noise ratio. In a thorough empirical study, with simulated and real-world physiological data sets, our method is shown to be more accurate than existing algorithms at detecting the patterns' onsets.

关键词： compressive statistical learning convolutional sparse coding L0 penalty signal processing

来源：评论

学校读者我要写书评

暂无评论

PROJECTING ONTO THE MULTI-LAYER convolutional sparse coding MODEL

PROJECTING ONTO THE MULTI-LAYER CONVOLUTIONAL SPARSE CODING ...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Sulam, Jeremias Papyan, Vardan Romano, Yaniv Elad, Michael Technion Israel Inst Technol Comp Sci Dept Haifa Israel Technion Israel Inst Technol Elect Engn Dept Haifa Israel

ISBN: (纸本)9781538646588

The recently proposed Multi-Layer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse layers, provides a new interpretation of convolutional Neural Networks (CNNs). Under this framework, the forward pass in a CNN is equivalent to an algorithm that estimates nested sparse representation vectors from a given input signal. Despite having served as a pivotal connection between CNNs and sparse modeling, it is still unclear how to develop pursuit algorithms that serve this model exactly. In this work, we propose a new pursuit formulation by adopting a projection approach. We provide new and improved bounds on the stability of the resulting convolutional sparse representations, and we propose a multi-layer projection algorithm to retrieve them. We demonstrate this algorithm numerically, showing that it is superior to the Layered Basis Pursuit alternative in retrieving the representations of signals belonging to the ML-CSC model.

关键词： convolutional sparse coding Multi-layer Pursuit Stability Guarantees convolutional Neural Networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：