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2nd International Workshop on Networked AI Systems (NetAISys)

作者： Chen, Siya Tan, Chee Wei City Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Nanyang Ave Singapore Singapore

ISBN: (纸本)9798400706615

In this paper, we introduce Neural Sum Rate Maximization to address nonconvex problems in maximizing sum rates with a total power constraint for downlink multiple access. We combine the optimization-theoretic methods and neural network-based algorithm unrolling techniques to map iterations onto trainable neural network layers, enabling development and deployment in the AI-native layer for future wireless networks. Our approach leverages mathematical structures in sum rate optimization, such as the alternating direction method of multipliers and the standard interference function framework for iterative algorithm design. By integrating algorithm unrolling techniques, our approach can learn from data and significantly enhance efficiency. Numerical experiments demonstrate the method's advantages in efficiency, performance, and interpretability, which are particularly beneficial for resource-constrained optimization in AI-native wireless networks.

关键词： Sum rate maximization Alternating direction method of multipliers Majorization-minimization algorithm unrolling

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Photon Limited Non-Blind Deblurring Using algorithm unrolling

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2022年 8卷 851-864页

作者： Sanghvi, Yash Gnanasambandam, Abhiram Chan, Stanley H. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Purdue Univ W Lafayette IN 47907 USA

Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of the photon shot noise due to the low illumination and/or short exposure makes the deblurring task substantially more challenging than the conventional deblurring problems. In this paper, we present an algorithm unrolling approach for the photon-limited deblurring problem by unrolling a Plug-and-Play algorithm for a fixed number of iterations. By introducing a three-operator splitting formation of the Plug-and-Play framework, we obtain a series of differentiable steps which allows the fixed iteration unrolled network to be trained end-to-end. The proposed algorithm demonstrates significantly better image recovery compared to existing state-of-the-art deblurring approaches. We also present a new photon-limited deblurring dataset for evaluating the performance of algorithms.

关键词： Photon limited poisson deconvolution deblurring plug-and-play algorithm unrolling

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Efficient and Model-Based Infrared and Visible Image Fusion via algorithm unrolling

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第3期32卷 1186-1196页

作者： Zhao, Zixiang Xu, Shuang Zhang, Jiangshe Liang, Chengyang Zhang, Chunxia Liu, Junmin Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as algorithm unrolling Image Fusion (AUIF), is proposed to overcome the shortcomings of traditional CNN-based IVIF models. The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i.e., separating low-frequency base information and high-frequency detail information from source images. Then the algorithm unrolling is implemented where each iteration is mapped to a CNN layer and each optimization model is transformed into a trainable neural network. Compared with the general network architectures, the proposed framework combines the model-based prior information and is designed more reasonably. After the unrolling operation, our model contains two decomposers (encoders) and an additional reconstructor (decoder). In the training phase, this network is trained to reconstruct the input image. While in the test phase, the base (or detail) decomposed feature maps of infrared/visible images are merged respectively by an extra fusion layer, and then the decoder outputs the fusion image. Qualitative and quantitative comparisons demonstrate the superiority of our model, which can robustly generate fusion images containing highlight targets and legible details, exceeding the state-of-the-art methods. Furthermore, our network has fewer weights and faster speed.

关键词： Optimization Image fusion Feature extraction Task analysis Kernel Decoding Neural networks Image fusion two-scale decomposition algorithm unrolling model-based network structure

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PHOTON-LIMITED DEBLURRING USING algorithm unrolling 47

PHOTON-LIMITED DEBLURRING USING ALGORITHM UNROLLING

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

作者： Sanghvi, Yash Gnanasambandan, Abhiram Chan, Stanley H. Purdue Univ Sch Elect & Comp Engn 465 Northwestern Ave W Lafayette IN 47907 USA

ISBN: (纸本)9781665405409

Image deblurring in a photon-limited condition is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of photon shot noise due to a low-illumination and/or short exposure makes the deblurring task substantially more challenging than conventional deblurring. In this paper we present an algorithm unrolling approach that unrolls a Plug-and-Play algorithm using a fixed-iteration network. By changing the conventional two-variable splitting formulation of Plug-and-Play to an alternate three-variable splitting, we obtain a differentiable and end-to-end trainable network. Our algorithm outperforms existing methods for 1dB across different illuminations. We also overcome the difficulty of acquiring real motion blur kernels at low-light by presenting a photon-limited motion deblurring image dataset.

关键词： Poisson deconvolution non-blind deblurring photon-limited imaging algorithm unrolling

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Non-Uniform Blind Image Deblurring Using an algorithm unrolling Neural Network 14

Non-Uniform Blind Image Deblurring Using an Algorithm Unroll...

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14th IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

作者： Richmond, Greig Cole-Rhodes, Arlene Morgan State Univ Baltimore MD 21239 USA

ISBN: (纸本)9781665478229

In this work we develop a compact neural network that is designed to deblur images that have been affected by a non-uniform blur. We develop this network by unrolling a traditional iterative image deblurring algorithm and adapt it to independently deblur regions of an image. The network is evaluated by comparing its deblurring capabilities with that of other state-of-the-art networks, such as the SRN-Deblur network [13] and the DUBLID network [4]. We investigate the effect that varying the patch size and the size of the point spread function has on the deblurring performance of our network. We evaluate each deblurring network using the industry standard GOPRO [12] and Kohler [25] datasets.

关键词： algorithm unrolling Convolutional neural network Image deblurring Image restoration Non-uniform blur

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Interpretable Neural Network via algorithm unrolling for Mechanical Fault Diagnosis

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： An, Botao Wang, Shibin Zhao, Zhibin Qin, Fuhua Yan, Ruqiang Chen, Xuefeng Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Artificial neural network (ANN) has achieved great success in mechanical fault diagnosis and has been widely used. However, traditional ANN is still opaque in terms of interpretability, making it difficult for users to understand and trust the diagnosis results. This article proposes an interpretable neural network to provide high-performance and credible mechanical fault diagnosis results. The proposed network is mainly generated by unrolling the nested iterative soft thresholding algorithm (NISTA) for a sparse coding model and it is named NISTA-Net. Therefore, the network architecture of NISTA-Net has a clear theoretical basis, and users know how it is designed. Additionally, we propose a visualization method for NISTA-Net to examine whether the network has learned meaningful features. This method helps users better understand how NISTA-Net performs classifications. These two aspects of transparency/interpretability allow NISTA-Net to be more credible when applied for mechanical fault diagnosis. We carried out simulations and two experiments of fault diagnosis to verify the performance of NISTA-Net. The results reveal that NISTA-Net can well extract the fault features of the concerned bearings and gears. As a consequence, it achieves the best performance compared with other advanced networks. Given the success of NISTA-Net, a systematic approach is finally summarized to help design interpretable fault diagnosis networks, aiming to inspire more related research.

关键词： Feature extraction Fault diagnosis Encoding Vibrations Convolutional codes Classification algorithms Ad hoc networks algorithm unrolling interpretable neural network mechanical fault diagnosis prognostic and health management (PHM) sparse coding

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Efficient and Interpretable Deep Blind Image Deblurring Via algorithm unrolling

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2020年 6卷 666-681页

作者： Li, Yuelong Tofighi, Mohammad Geng, Junyi Monga, Vishal Eldar, Yonina C. Penn State Univ Dept Elect Engn University Pk PA 16802 USA Penn State Univ Dept Aerosp Engn University Pk PA 16802 USA Weizmann Inst Sci Fac Math & Comp Sci Dept Elect Engn IL-76100 Rehovot Israel

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. In this article, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability and efficiency at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.

关键词： Image deblurring deconvolution deep learning algorithm unrolling interpretable deep networks

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GSISTA-Net: generalized structure ISTA networks for image compressed sensing based on optimized unrolling algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第34期83卷 80373-80387页

作者： Zeng, Chunyan Yu, Yan Wang, Zhifeng Xia, Shiyan Cui, Hao Wan, Xiangkui Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Nanli Rd Wuhan 430068 Peoples R China Cent China Normal Univ Dept Digital Media Technol Luoyu Rd Wuhan 430079 Peoples R China

Image compressed sensing technology, particularly algorithm unrolling networks, has garnered significant attention in the field of compressed sensing due to their interpretability and high performance. However, similar to traditional compressed sensing methods, algorithm unrolling networks update and transmit pixel-level image data through specific instances of algorithms, often failing to fully exploit the rich information encoded in image features. This limitation results in information loss and incomplete feature fusion. In this paper, we introduce a novel approach: the Generalized Structure Iterative Shrinkage Threshold algorithm (GSISTA), and present an algorithm extension network built upon GSISTA, referred to as GSISTA-Net. GSISTA-Net facilitates the efficient transfer of image feature information during the deep reconstruction stage through a jump connection structure. Additionally, it incorporates a dual-scale denoising module within the deep reconstruction stage to enhance denoising effectiveness. Our experimental results demonstrate that the proposed method surpasses the performance of five prominent state-of-the-art algorithms, specifically ReconNet, CSNet, ISTA-Net+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>+$$\end{document}, AMPNet, and OPINE-Net+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>+$$\end{document}, when evaluated in terms of both Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

关键词： algorithm unrolling Compressed sensing Image reconstruction Deep learning

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A Bayesian Based Deep unrolling algorithm for Single-Photon Lidar Systems

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第4期16卷 762-774页

作者： Koo, Jakeoung Halimi, Abderrahim McLaughlin, Stephen Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland

Deploying 3D single-photon Lidar imaging in real world applications presents multiple challenges including imaging in high noise environments. Several algorithms have been proposed to address these issues based on statistical or learning-based frameworks. Statistical methods provide rich information about the inferred parameters but are limited by the assumed model correlation structures, while deep learning methods show state-of-the-art performance but limited inference guarantees, preventing their extended use in critical applications. This paper unrolls a statistical Bayesian algorithm into a new deep learning architecture for robust image reconstruction from single-photon Lidar data, i.e. the algorithm's iterative steps are converted into neural network layers. The resulting algorithm benefits from the advantages of both statistical and learning based frameworks, providing best estimates with improved network interpretability. Compared to existing learning-based solutions, the proposed architecture requires a reduced number of trainable parameters, is more robust to noise and mismodelling of the system impulse response function, and provides richer information about the estimates including uncertainty measures. Results on synthetic and real data show competitive results regarding the quality of the inference and computational complexity when compared to state-of-the-art algorithms.

关键词： Laser radar Photonics Deep learning Signal processing algorithms Three-dimensional displays Bayes methods Histograms 3D reconstruction Lidar single-photon imaging algorithm unrolling attention Bayesian inference

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Multi-frequency progressive refinement for learned inverse scattering

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JOURNAL OF COMPUTATIONAL PHYSICS 2025年 527卷

作者： Melia, Owen Tsang, Olivia Charisopoulos, Vasileios Khoo, Yuehaw Hoskins, Jeremy Willett, Rebecca Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Chicago Data Sci Inst Chicago IL 60637 USA Univ Chicago Dept Stat Computat & Appl Math Chicago IL 60637 USA

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method - a commonly used technique for stably inverting scattered wavefield data - that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds.

关键词： Machine learning Inverse scattering algorithm unrolling Deep learning Inverse problems

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