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检索条件"主题词=algorithm unrolling"

共 71 条记录，以下是51-60 订阅

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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

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unrolling Alternating Direction Method of Multipliers for Visible and Infrared Image Fusion 5

Unrolling Alternating Direction Method of Multipliers for Vi...

引用

5th IEEE International Conference on Image Processing Applications and Systems (IPAS)

作者： Bakan, Altug Erer, Isin Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey ASELSAN Microelect Guidance & Electroopt Business Sect Ankara Turkey

ISBN: (纸本)9781665462198

In this paper a new infrared and visible image fusion (IVIF) method which combines the advantages of optimization and deep learning based methods is proposed. This model takes the iterative solution used by the alternating direction method of the multiplier (ADMM) optimization method, and uses algorithm unrolling to obtain a high performance and efficient algorithm. Compared with traditional optimization methods, this model generates fusion with 99.6% improvement in terms of image fusion time, and compared with deep learning based algorithms, this model generates detailed fusion images with 99.1% improvement in terms of training time. Compared with the other state-of-the-art unrolling based methods, this model performs 26.7% better on average in terms of Average Gradient (AG), Cross Entropy (CE), Mutual Information (MI), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Loss (SSIM) metrics with a minimal testing time cost.

关键词： visible and infrared image fusion algorithm unrolling model based deep network ADMM

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unrolling PARTICLES: UNSUPERVISED LEARNING OF SAMPLING DISTRIBUTIONS 47

UNROLLING PARTICLES: UNSUPERVISED LEARNING OF SAMPLING DISTR...

引用

47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Gama, Fernando Zilberstein, Nicolas Baraniuk, Richard G. Segarra, Santiago Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA

ISBN: (纸本)9781665405409

Particle filtering is used to compute nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted average of them. Easy-to-sample distributions often lead to degenerate samples where only one trajectory carries all the weight, negatively affecting the resulting performance of the estimate. While much research has been done on the design of appropriate sampling distributions that would lead to controlled degeneracy, in this paper our objective is to learn sampling distributions. Leveraging the framework of algorithm unrolling, we model the sampling distribution as a multivariate normal, and we use neural networks to learn both the mean and the covariance. We carry out unsupervised training of the model to minimize weight degeneracy, relying only on the observed measurements of the system. We show in simulations that the resulting particle filter yields good estimates in a wide range of scenarios.

关键词： algorithm unrolling particle filtering unsupervised learning

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CG-Net: A Compound Gaussian Prior Based Unrolled Imaging Network

CG-Net: A Compound Gaussian Prior Based Unrolled Imaging Net...

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14th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Lyons, Carter Raj, Raghu G. Cheney, Margaret US Naval Res Lab Washington DC 20375 USA Colorado State Univ Ft Collins CO USA

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

In the age of accessible computing, machine intelligence (MI) has become a widely applicable and successful tool in image recognition. With this success, MI has, more recently, been applied to compressive sensing and tomographic imaging. One particular application of MI to image estimation, known as algorithm unrolling, is the implementation of an iterative imaging algorithm as a deep neural network (DNN). algorithm unrolling has shown improvements in image reconstruction over both iterative imaging algorithms and standard neural networks. Here, we present a least squares iterative image estimation algorithm under the assumption of a Compound Gaussian (CG) prior for the image. The CG prior asserts that the image wavelet coefficients are a nonlinear function of two Gaussians. The developed iterative imaging algorithm is then unrolled into a DNN named CG-Net. After training, CG-Net is shown to be successful in the estimation of image wavelet coefficients from Radon transform measurements.

关键词： Deep learning compressive sensing imaging inverse problems algorithm unrolling

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DCT-ISTA: A Network Block to Denoise Raw Vibration Signal with Interpretability for Mechanical Fault Diagnosis 21

DCT-ISTA: A Network Block to Denoise Raw Vibration Signal wi...

引用

21st IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhao, Yujie Wang, Shibin Wang, Yujin Cao, Zhi Xi An Jiao Tong Univ Sch Mech Engn Xian Peoples R China Tongji Univ Sch Automot Studies Shanghai Peoples R China

ISBN: (纸本)9798350388084;9798350388077

Mechanical fault diagnosis is a crucial task in the field of high-value equipment prognostic and health management. Despite the numerous applications of deep neural networks in this domain, their lack of interpretability hinders their further adoption. In this paper, we propose a novel interpretable network block named DCT-ISTA for the diagnosis of rotating machinery faults, which is obtained by unrolling the classical Iterative Soft Thresholding algorithm from the compressive sensing field and integrating prior knowledge from the fault diagnosis domain. DCT-ISTA combines sparse coding with Discrete Cosine Transform (DCT), formulated as a network through algorithmic unrolling, and integrates it as a data denoising method with backbone networks to enhance their performance. We validate the proposed method using simulated data with three different backbone networks. Through accuracy evaluation and analysis of signal reconstruction results, we demonstrate the effectiveness of our approach.

关键词： Mechanical fault diagnosis algorithm unrolling Wavelet transform interpretable neural network sparse coding

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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

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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

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DEEP UNFOLDING NETWORK WITH PHYSICS-BASED PRIORS FOR UNDERWATER IMAGE ENHANCEMENT 30

DEEP UNFOLDING NETWORK WITH PHYSICS-BASED PRIORS FOR UNDERWA...

引用

30th IEEE International Conference on Image Processing (ICIP)

作者： Thuy Thi Pham Truong Thanh Nhat Mai Lee, Chul Dongguk Univ Dept Multimedia Engn Seoul South Korea

ISBN: (纸本)9781728198354

We propose an underwater image enhancement algorithm that leverages both model- and learning-based approaches by unfolding an iterative algorithm. We first formulate the underwater image enhancement task as a joint optimization problem, based on the image formation model with physical model and underwater-related priors. Then, we solve the optimization problem iteratively. Finally, we unfold the iterative algorithm so that, at each iteration, the optimization variables and regularizers for image priors are updated by closed-form solutions and learned deep networks, respectively. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art underwater image enhancement algorithms.

关键词： Underwater image enhancement algorithm unrolling prior learning model-based deep learning

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MCAN: Interpretable Multi-scale Component Analysis Network for Mechanical Fault Diagnosis

MCAN: Interpretable Multi-scale Component Analysis Network f...

引用

IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Rising Above Covid-19

作者： Qin, Fuhua Wang, Shiao Wang, Shibin Zhao, Zhibin Yan, Ruqiang Chen, Xuefeng Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Peoples R China

ISBN: (纸本)9781665453837

Deep neural networks have provided unprecedented performance gains in the field of mechanical fault diagnosis. However, the practical deployment of deep networks is hindered by their black-box nature, i.e. lack of interpretability. In this paper, a multi-scale component analysis Network (MCAN) with high performance and interpretability was proposed for mechanical equipment fault diagnosis. MCAN is developed by unrolling the iterative solution algorithm of a morphological component analysis (MCA) model that incorporates multi-scale priors of the vibration signals into a network, so its network architecture is interpretable. In addition, this paper designs a feature visualization method to verify whether the network extracts meaningful features corresponding to fault mechanisms. Experimental verification results show that compared with other advanced networks, MCAN has the best performance and can effectively extract fault features.

关键词： Interpretable neural network morphological component analysis (MCA) algorithm unrolling prognostic and health management (PHM)

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A CONVERGENT NEURAL NETWORK FOR NON-BLIND IMAGE DEBLURRING 30

A CONVERGENT NEURAL NETWORK FOR NON-BLIND IMAGE DEBLURRING

引用

30th IEEE International Conference on Image Processing (ICIP)

作者： Zhao, Yanan Li, Yuelong Zhang, Haichuan Monga, Vishal Eldar, Yonina C. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Amazon Sunnyvale CA USA Penn State Univ Dept Elect Engn University Pk PA 16802 USA Weizmann Inst Sci Fac Math & Comp Sci Rehovot Israel

ISBN: (纸本)9781728198354

In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling based deep network design since many traditional model-based approaches rely on iterative optimization. Despite exciting progress, typical unrolling approaches heuristically design layer-specific convolution weights to improve performance. Crucially, convergence properties of the underlying iterative algorithm are lost once layer specific parameters are learned from training data. In this paper, we propose a neural network architecture that breaks the trade-off between retaining algorithm properties while simultaneously enhancing performance. We focus on non-blind image deblurring problem and unroll the widely-applied Half-Quadratic Splitting (HQS) algorithm. We develop a new parameterization scheme that enforces the layer-specific parameters to asymptotically approach certain fixed points, a new result that we analytically establish. Experimental results show that our approach outperforms many state of the art non-blind deblurring techniques on benchmark datasets, while enabling convergence and interpretability.

关键词： Image deblurring algorithm unrolling deep neural networks

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