检索结果-内蒙古大学图书馆

Deep Interpretable Fully CNN Structure for Sparse Hyperspectral Unmixing via Model-Driven and Data-Driven Integration

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1-1页

作者： Kong, Fanqiang Chen, Mengyue Li, Yunsong Li, Dan Zheng, Yuhan Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xidian 710071 Peoples R China

Hyperspectral unmixing (HSU), which aims to identify constituent materials and estimate the corresponding proportions in a scene, is an essential research topic in remote sensing. Most deep learning-based methods are data-inspired, relying on massive amounts of data to train black-box-like networks, while a few model-inspired unmixing networks only consider the spectral features of the pixel, ignoring the exploration of spatial information between pixels. In this article, we design a network topology according to the classical iterative algorithm, and the large number of learnable parameters contained in the network is continuously updated through data fitting. In other words, we integrate the concepts of both model- and data-driven and propose a deep interpretable fully convolutional neural network (DIFCNN). The iteration of the classic sparse unmixing algorithm is unfolded to provide guidance for the network structure and incorporate prior knowledge into the network. Meanwhile, 2-D convolutional layers are employed to automatically learn the spatial information at different scales. A known spectral library is used as a prior to initialize network parameters and reconstruct the image. The DIFCNN adopts an end-to-end training strategy;in addition, we establish a new loss function that adds a joint sparse constraint on the abundance result to the cross-entropy loss. Experiments on both synthetic and real datasets show that the performance of the DIFCNN not only outperforms the sparse unmixing by variable splitting and augmented Lagrangian (SUnSAL) and its improved algorithms but also is highly competitive in the state-of-the-art methods of deep learning.

关键词： algorithm unrolling convolutional neural network (CNN) hyperspectral unmixing (HSU) model-driven spectral-spatial

来源：评论

学校读者我要写书评

暂无评论

Learning-Based High-Frame-Rate SAR Imaging

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Wu, Junjie Pu, Wei An, Hongyang Huang, Yulin Yang, Haiguang Yang, Jianyu Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

As high-frame-rate synthetic aperture radar (SAR) has the ability to form continuous SAR images and dynamically monitor the ground areas of interest, it has attracted more and more attention nowadays. In practical applications, the enormous data in high-frame-rate SAR system to obtain the multiframe images brings big challenges to its transmission, storage, and processing. In order to solve this problem, there are many recent papers on formulating the high-frame-rate SAR imaging problem into a low-rank tensor recovery problem, and correspondingly, the sampling amount of the high-frame-rate SAR data can be largely reduced. However, existing algorithms to solve the low-rank tensor recovery problem in high-frame-rate SAR application still suffer from large computational cost. Under the above inspiration, this article proposes a deep neural network architecture for high-frame-rate SAR imaging, i.e., tensor alternating direction method of multiplier network (TADMM-Net), which is more computationally efficient in the imaging procedure. Specifically, we formulate the high-frame-rate SAR imaging processing into a low-tubal-rank tensor recovery problem. We solve the low-tubal-rank tensor recovery problem using a tensor alternating direction method of multiplier (ADMM) algorithm and then design a new deep neural network architecture by applying algorithm unfolding techniques to the underlying low-rank tensor recovery problem. The proposed TADMM-Net approach shifts the computational burden from the testing phase to the training phase, and the practical processing time can be extremely decreased compared with the existing algorithms for the low-rank tensor recovery problem in high-frame-rate SAR imaging applications. It also offers various advantages over the existing high-frame-rate SAR imaging algorithms, including higher performance in the case with low sampling amount, lower storage complexity, and no requirement for handcraft hyperparameters adjustment. The methodology was

关键词： Tensors Radar polarimetry Synthetic aperture radar Imaging Optimization Signal processing algorithms Signal processing algorithm unrolling deep neural network high-frame-rate synthetic aperture radar (SAR) tensor alternating direction method of multiplier network (TADMM-Net)

来源：评论

学校读者我要写书评

暂无评论

MODEL-INSPIRED DEEP LEARNING FOR LIGHT-FIELD MICROSCOPY WITH APPLICATION TO NEURON LOCALIZATION

MODEL-INSPIRED DEEP LEARNING FOR LIGHT-FIELD MICROSCOPY WITH...

引用

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

作者： Song, Pingfan Jadan, Herman Verinaz Howe, Carmel L. Quicke, Peter Foust, Amanda J. Dragotti, Pier Luigi Imperial Coll London Dept Elect & Elect Engn London England Imperial Coll London Dept Bioengn London England Imperial Coll London Ctr Neurotechnol London England

ISBN: (纸本)9781728176055

Light-field microscopes are able to capture spatial and angular information of incident light rays. This allows reconstructing 3D locations of neurons from a single snap-shot. In this work, we propose a model-inspired deep learning approach to perform fast and robust 3D localization of sources using light-field microscopy images. This is achieved by developing a deep network that efficiently solves a convolutional sparse coding (CSC) problem to map Epipolar Plane Images (EPI) to corresponding sparse codes. The network architecture is designed systematically by unrolling the convolutional Iterative Shrinkage and Thresholding algorithm (ISTA) while the network parameters are learned from a training dataset. Such principled design enables the deep network to leverage both domain knowledge implied in the model, as well as new parameters learned from the data, thereby combining advantages of model-based and learning-based methods. Practical experiments on localization of mammalian neurons from light-fields show that the proposed approach simultaneously provides enhanced performance, interpretability and efficiency.

关键词： Light-field microscopy model-inspired deep learning algorithm unrolling sparse coding

来源：评论

学校读者我要写书评

暂无评论

Decentralized Statistical Inference with Unrolled Graph Neural Networks 60

Decentralized Statistical Inference with Unrolled Graph Neur...

引用

60th IEEE Conference on Decision and Control (CDC)

作者： Wang, He Shen, Yifei Wang, Ziyuan Li, Dongsheng Zhang, Jun Letaief, Khaled B. Lu, Jie ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Microsoft Res Asia Shanghai Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781665436595

In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples without centralized coordination. Existing optimization-based algorithms suffer from issues of model mismatches and poor convergence speed, and thus their performance would be degraded provided that the number of communication rounds is limited. This motivates us to propose a learning-based framework, which unrolls well-noted decentralized optimization algorithms (e.g., Prox-DGD and PG-EXTRA) into graph neural networks (GNNs). By minimizing the recovery error via end-to-end training, this learning-based framework resolves the model mismatch issue. Our convergence analysis (with PG-EXTRA as the base algorithm) reveals that the learned model parameters may accelerate the convergence and reduce the recovery error to a large extent. The simulation results demonstrate that the proposed GNN-based learning methods prominently outperform several state-of-the-art optimization-based algorithms in convergence speed and recovery error.

关键词： Decentralized optimization graph neural networks algorithm unrolling interpretable deep learning

来源：评论

学校读者我要写书评

暂无评论

GRAPH SIGNAL DENOISING VIA unrolling NETWORKS

GRAPH SIGNAL DENOISING VIA UNROLLING NETWORKS

引用

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

作者： Chen, Siheng Eldar, Yonina C. Mitsubishi Elect Res Labs Cambridge MA 02139 USA Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781728176055

We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to the graph domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals. We train the graph unrolling networks through unsupervised learning, where the input noisy graph signals are used to supervise the networks. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input noisy graph signals, instead of manually choosing signal priors. To validate the proposed methods, we conduct extensive experiments on both real-world datasets and simulated datasets, and demonstrate that our methods have smaller denoising errors than conventional denoising algorithms and state-of-the-art graph neural networks. For denoising a single smooth graph signal, the normalized mean square error of the proposed networks is around 40% and 60% lower than that of graph Laplacian denoising and graph wavelets, respectively.

关键词： Graph signal denoising algorithm unrolling graph neural networks

来源：评论

学校读者我要写书评

暂无评论

TIME-VARYING GRAPH SIGNAL INPAINTING VIA unrolling NETWORKS

TIME-VARYING GRAPH SIGNAL INPAINTING VIA UNROLLING NETWORKS

引用

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

作者： Chen, Siheng Eldar, Yonina C. Mitsubishi Elect Res Labs Cambridge MA 02139 USA Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781728176055

We propose an interpretable graph neural network based on algorithm unrolling to reconstruct a time-varying graph signal from partial measurements. The proposed graph unrolling networks expand algorithm unrolling to the graph-time domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative inpainting algorithm by mapping each iteration to a single network layer. The feed-forward process is thus equivalent to iteratively reconstructing a time-varying graph signal. We train this network through unsupervised learning, where the input time-varying graph signal is used to supervise the training. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input data, instead of manually choosing signal priors. To validate the proposed methods, we conduct experiments on three real-world datasets and demonstrate that our networks achieve smaller reconstruction errors than conventional inpainting algorithms and state-of-the-art graph neural networks.

关键词： Graph-temporal data Graph neural networks algorithm unrolling

来源：评论

学校读者我要写书评

暂无评论

AN ADMM BASED NETWORK FOR HYPERSPECTRAL UNMIXING TASKS

AN ADMM BASED NETWORK FOR HYPERSPECTRAL UNMIXING TASKS

引用

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

作者： Zhou, Chao Rodrigues, Miguel R. D. UCL Dept Elect & Elect Engn London England

ISBN: (纸本)9781728176055

In this paper, we use algorithm unrolling approaches in order to design a new neural network structure applicable to hyperspectral unmixing challenges. In particular, building upon a constrained sparse regression formulation of the underlying unmixing problem, we unroll an ADMM solver onto a neural network architecture that can be used to deliver the abundances of different (known) endmembers given a reflectance spectrum. Our proposed network - which can be readily trained using standard supervised learning procedures - is shown to possess a richer structure consisting of various skip connections and shortcuts than other competing architectures. Moreover, our proposed network also delivers state-of-the-art unmixing performance compared to competing methods.

关键词： HSI unmixing Deep Neural Networks algorithm unrolling algorithm Unfolding

来源：评论

学校读者我要写书评

暂无评论

IMPROVED SUPERVISED TRAINING OF PHYSICS-GUIDED DEEP LEARNING IMAGE RECONSTRUCTION WITH MULTI-MASKING

IMPROVED SUPERVISED TRAINING OF PHYSICS-GUIDED DEEP LEARNING...

引用

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

作者： Yaman, Burhaneddin Hosseini, Seyed Amir Hossein Moeller, Steen Akcakaya, Mehmet Univ Minnesota Elect & Comp Engn Minneapolis MN 55455 USA Univ Minnesota Ctr Magnet Resonance Res Minneapolis MN 55455 USA

ISBN: (纸本)9781728176055

Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications. These methods unroll an iterative optimization algorithm into a series of regularizer and data consistency units. The unrolled networks are typically trained end-to-end using a supervised approach. Current supervised PG-DL approaches use all of the available sub-sampled measurements in their data consistency units. Thus, the network learns to fit the rest of the measurements. In this study, we propose to improve the performance and robustness of supervised training by utilizing randomness by retrospectively selecting only a subset of all the available measurements for data consistency units. The process is repeated multiple times using different random masks during training for further enhancement. Results on knee MRI show that the proposed multi-mask supervised PG-DL enhances reconstruction performance compared to conventional supervised PG-DL approaches.

关键词： Supervised learning algorithm unrolling physics-guided deep learning accelerated imaging magnetic resonance imaging data augmentation

来源：评论

学校读者我要写书评

暂无评论

Learning the sparse prior: Modern approaches

引用

Wiley Interdisciplinary Reviews: Computational Statistics 2024年第1期16卷 e1646-e1646页

作者： Peng, Guan-Ju Institute of Data Science and Information Computing National Chung Hsing University Taichung Taiwan

The sparse prior has been widely adopted to establish data models for numerous applications. In this context, most of them are based on one of three foundational paradigms: the conventional sparse representation, the convolutional sparse representation, and the multi-layer convolutional sparse representation. When the data morphology has been adequately addressed, a sparse representation can be obtained by solving the sparse coding problem specified by the data model. This article presents a comprehensive overview of these three models and their corresponding sparse coding problems and demonstrates that they can be solved using convex and non-convex optimization approaches. When the data morphology is not known or cannot be analyzed, it must be learned from training data, thereby formulating dictionary learning problems. This article addresses two different dictionary learning paradigms. In an unsupervised scenario, dictionary learning involves the alternating or joint resolution of sparse coding and dictionary updating. Another option is to create a recurrent neural network by unrolling algorithms designed to solve sparse coding problems. These networks can then be used in a supervised learning setting to facilitate the training of dictionaries via forward-backward optimization. This article lists numerous applications in various domains and outlines several directions for future research related to the sparse prior. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Modeling Methods and algorithms Statistical Models > Nonlinear Models. © 2024 Wiley Periodicals LLC.

关键词： algorithm unrolling convex and non-convex optimization convolutional sparse model dictionary learning multi-layer convolutional sparse model recurrent neural network sparse coding sparse prior

来源：评论

学校读者我要写书评

暂无评论

Learning to optimize: a primer and a benchmark

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2022年第1期23卷 8562-8620页

作者： Tianlong Chen Xiaohan Chen Wuyang Chen Zhangyang Wang Howard Heaton Jialin Liu Wotao Yin Department of Electrical and Computer and Engineering The University of Texas at Austin Austin TX Typal Research Typal LLC Los Angeles CA Alibaba US Damo Academy Decision Intelligence Lab Bellevue WA

Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods, aiming at reducing the laborious iterations of hand engineering. It automates the design of an optimization method based on its performance on a set of training problems. This data-driven procedure generates methods that can efficiently solve problems similar to those in training. In sharp contrast, the typical and traditional designs of optimization methods are theory-driven, so they obtain performance guarantees over the classes of problems speci_ed by the theory. The difference makes L2O suitable for repeatedly solving a particular optimization problem over a specific distribution of data, while it typically fails on out-of-distribution problems. The practicality of L2O depends on the type of target optimization, the chosen architecture of the method to learn, and the training procedure. This new paradigm has motivated a community of researchers to explore L2O and report their *** article is poised to be the first comprehensive survey and benchmark of L2O for continuous optimization. We set up taxonomies, categorize existing works and research directions, present insights, and identify open challenges. We benchmarked many existing L2O approaches on a few representative optimization problems. For reproducible research and fair benchmarking purposes, we released our software implementation and data in the package Open-L2O.

关键词： learning to optimize meta learning optimization algorithm unrolling

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：