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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Fu, Guanyiman Xiong, Fengchao Tao, Shuyin Lu, Jianfeng Zhou, Jun Qian, Yuntao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Griffith Univ Sch Informat & Commun Technol Gold Coast Australia Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9781665403696

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the quality of HSI. model-based methods take the degradation model and the structure of underlying clean HSI into account for denoising but require a large number of numerical iterations and exhausting parameter tuning. deep-learning-based (DL-based) methods directly learn the nonlinear transformation of clean and noisy image HSI pairs, but rely on large-scale high-quality training samples because of its "black box" denoising mechanism. In this paper, we propose a model-based DL method for HSI denoising to combine the advantages of model-based methods and DL-based methods. Specifically, we first build a HSI denoising model based on sparse representation. Then, we unfold the iterative optimization under the framework of gradient descent with momentum to yield a Gradient Momentum Sparse Coding Network (GMSC-Net) for denoising. In order to overcome the unavailability of noisy-clean HSI pairs for training, we directly learn GMSC-Net from a single HSI. The observed noisy HSI is grouped into a number of clusters containing local cubes. The cluster centers are treated as "clean" cubes and are polluted by noises, yielding a set of "noisy-clean" pairs for training. Extensive experiments show the effectiveness of our method on both synthetic and real-world datasets.

关键词： Hyperspectral image denoising model-based deep learning sparse coding

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Modular Hypernetworks for Scalable and Adaptive deep MIMO Receivers

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2025年 6卷 256-265页

作者： Raviv, Tomer Shlezinger, Nir Ben Gurion Univ Negev Sch ECE IL-8410501 Negev Israel

deep neural networks (DNNs) were shown to facilitate the operation of uplink multiple-input multiple-output (MIMO) receivers, with emerging architectures augmenting modules of classic receiver processing. Current designs employ static DNNs, whose architecture is fixed and weights are pre-trained. This poses a notable challenge, as the resulting MIMO receiver is suitable for a given configuration, i.e., channel distribution and number of users, while in practice these parameters change frequently with network variations and users leaving and joining the network. In this work, we tackle this core challenge of DNN-aided MIMO receivers. We build upon the concept of hypernetworks, augmenting the receiver with a pre-trained deep model whose purpose is to update the weights of the DNN-aided receiver in response to instantaneous channel variations. We design our hypernetwork to augment modular deep receivers, leveraging their modularity to have the hypernetwork adapt not only the weights, but also the architecture. Our modular hypernetwork leads to a DNN-aided receiver whose architecture and resulting complexity adapt to the number of users, as well as to channel variations, without re-training. Our numerical studies demonstrate superior error-rate performance of modular hypernetworks in time-varying channels compared to static pre-trained receivers, while providing rapid adaptivity and scalability to network variations.

关键词： Receivers Training Symbols Wireless communication deep learning Computer architecture Complexity theory Adaptation models Vectors Uplink deep receivers hypernetworks MIMO model-based deep learning

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MBSS-T1: model-based subject-specific self-supervised motion correction for robust cardiac T1 mapping

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MEDICAL IMAGE ANALYSIS 2025年 102卷 103495页

作者： Hanania, Eyal Zehavi-Lenz, Adi Volovik, Ilya Link-Sourani, Daphna Cohen, Israel Freiman, Moti Technion IIT Fac Elect & Comp Engn Haifa Israel Technion IIT Fac Biomed Engn Haifa Israel Technion IIT Fac Biomed Engn May Blum Dahl MRI Res Ctr Haifa Israel Bnai Zion Med Ctr Haifa Israel

Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges with patient compliance, limiting their effectiveness. Image registration can enable motion-robust cardiac T1 mapping, but inherent intensity differences between time points pose a challenge. We present MBSST1, a subject-specific self-supervised model for motion correction in cardiac T1 mapping. Physical constraints, implemented through a loss function comparing synthesized and motion-corrected images, enforce signal decay behavior, while anatomical constraints, applied via a Dice loss, ensure realistic deformations. The unique combination of these constraints results in motion-robust cardiac T1 mapping along the longitudinal relaxation axis. Ina 5-fold experiment on a public dataset of 210 patients (STONE sequence) and an internal dataset of 19 patients (MOLLI sequence), MBSS-T1 outperformed baseline deep-learning registration methods. It achieved superior model fitting quality (R2: 0.975 vs. 0.941, 0.946 for STONE;0.987 vs. 0.982, 0.965 for MOLLI free- breathing;0.994 vs. 0.993, 0.991 for MOLLI breath-hold), anatomical alignment (Dice: 0.89 vs. 0.84, 0.88 for STONE;0.963 vs. 0.919, 0.851 for MOLLI free-breathing;0.954 vs. 0.924, 0.871 for MOLLI breath-hold), and visual quality (4.33 vs. 3.38, 3.66 for STONE;4.1 vs. 3.5, 3.28 for MOLLI free-breathing;3.79 vs. 3.15, 2.84 for MOLLI breath-hold). MBSS-T1 enables motion-robust T1 mapping for broader patient populations, overcoming challenges such as suboptimal compliance, and facilitates free-breathing cardiac T1 mapping without requiring large annotated datasets. Our code is available at https://***/TechnionComputationalMRILab/MBSST1.

关键词： T1 mapping Motion correction Myocardial imaging deep learning model-based deep learning Free-breathing MRI

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Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture

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MAGNETIC RESONANCE IN MEDICINE 2024年第5期92卷 2193-2206页

作者： Bartlett, Joseph J. Davey, Catherine E. Johnston, Leigh A. Duan, Jinming Univ Melbourne Graeme Clark Inst Melbourne Brain Ctr Imaging Unit Dept Biomed Engn Parkville Vic Australia Univ Birmingham Sch Comp Sci Birmingham England Alan Turing Inst London England Univ Birmingham Sch Comp Sci Univ Rd West Birmingham B15 2TT England

PurposeThe aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition *** proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical *** achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of *** of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

关键词： diffusion MRI fixel-based analysis FOD reconstruction model-based deep learning

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Guaranteed deep learning for Reliable Radar Signal Processing 12

Guaranteed Deep Learning for Reliable Radar Signal Processin...

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IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Khobahi, Shahin Mostajeran, Ali Emadi, Mohammad Wang, Pu Soltanalian, Mojtaba Univ Illinois Chicago IL 60607 USA Zadar Labs Inc Santa Clara CA USA Mitsubishi Elect Res Labs Cambridge MA USA

ISBN: (纸本)9781665406338

Recently, there has been a significant level of attention paid to the application of deep learning in radar signal processing. Despite its flexibility, deep learning imposes new challenges in guaranteeing the performance of signal processing systems and in establishing trust with regard to their outcome. This represents a critical bottleneck in the application of deep learning in radar signal processing, where having trust in radar inferences is crucial. In this work, we present a novel implicit deep learning model for learning fast and highly-scalable solvers for a general family of optimization problems commonly encountered in the radar signal processing area, encompassing applications in waveform design and target parameter estimation. Specifically, the proposed methodology is based on generalizing the feed-forward neural networks by introducing implicit layers derived from the dynamics of a fixed-point geometric series. Unlike its black-box data-driven counterparts, the implicit and model-based nature of the proposed neural solver significantly reduces the parameters of the network and implementation cost, and most importantly, makes the network amenable to robustness analysis, as well as deriving performance guarantees/bounds on the output of the model. This presents considerable potential for the adoption of our proposed models in radar applications, be it in the classical settings or emerging applications in autonomous vehicles and automotive radar where a large number of reliable radars are projected to be on the road in the near future.

关键词： Implicit deep learning Inference Guarantees model-based deep learning Radar Signal Processing Robustness and Reliability

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Local Monotone Operator learning Using Non-Monotone Operators: MnM-MOL

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024年 10卷 742-751页

作者： John, Maneesh Chand, Jyothi Rikhab Jacob, Mathews Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA

The recovery of magnetic resonance (MR) images from undersampled measurements is a key problem that has been the subject of extensive research in recent years. Unrolled approaches, which rely on end-to-end training of convolutional neural network (CNN) blocks within iterative reconstruction algorithms, offer state-of-the-art performance. These algorithms require a large amount of memory during training, making them difficult to employ in high-dimensional applications. deep equilibrium (DEQ) models and the recent monotone operator learning (MOL) approach were introduced to eliminate the need for unrolling, thus reducing the memory demand during training. Both approaches require a Lipschitz constraint on the network to ensure that the forward and backpropagation iterations converge. Unfortunately, the constraint often results in reduced performance compared to the unrolled methods. The main focus of this work is to relax the constraint on the CNN block in two different ways. Inspired by convex-non-convex regularization strategies, we now impose the monotone constraint on the sum of the gradient of the data term and the CNN block, rather than constrain the CNN itself to be a monotone operator. This approach enables the CNN to learn possibly non-monotone score functions, which can translate to improved performance. In addition, we only restrict the operator to be monotone in a local neighborhood around the image manifold. Our theoretical results show that the proposed algorithm is guaranteed to converge to the fixed point and that the solution is robust to input perturbations, provided that it is initialized close to the true solution. Our empirical results show that the relaxed constraints translate to improved performance and that the approach enjoys robustness to input perturbations similar to MOL.

关键词： model-based deep learning monotone operator learning deep equilibrium models

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learning to Sample: Data-Driven Sampling and Reconstruction of FRI Signals

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IEEE ACCESS 2023年 11卷 71048-71062页

作者： Mulleti, Satish Zhang, Haiyang Eldar, Yonina C. C. Indian Inst Technol Dept Elect Engn Mumbai 400076 India Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210049 Peoples R China Weizmann Inst Sci Fac Math & Comp Sci IL-210049 Rehovot Israel

Finite-rate-of-innovation (FRI) signal model is well suited for time-of-flight imaging applications such as ultrasound, lidar, sonar, radar, and more. Due to their finite degrees of freedom, the sub-Nyquist sampling framework is used for FRI signals. In this framework, sampling is achieved by using appropriate sampling kernels. Reconstruction is performed by first computing Fourier samples of the FRI signal and then applying sparse-recovery algorithms. The choice of the Fourier samples and reconstruction method plays a crucial role in the reconstruction quality. In this paper, we consider jointly optimizing the choice of Fourier samples and reconstruction parameters. Our framework combines a greedy subsampling algorithm and a learning-based sparse recovery method. The combination has three distinct advantages. First, the network does not require knowledge of the FRI pulse shape, which is not the case with existing approaches. Second, the network does not suffer from differentiability issues during training which is common in sampling networks. Further, the proposed algorithm can flexibly handle changes in the sampling rate. Numerical results show that, for a given number of samples, the proposed joint design leads to lower reconstruction error for FRI signals than independent data-driven design methods for noisy and clean samples. We also propose a way to extend the approach for large-scale problems. Our learning-to-sample approach can be readily applied to other sampling setups, including compressed sensing problems.

关键词： INDEX TERMS FRI signal sub-Nyquist sampling sum-of-sincs filter model-based deep learning unrolling greedy algorithm LISTA joint sampling and recovery learn to sample

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Graph Convolutional Networks for model-based learning in Nonlinear Inverse Problems

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2021年 7卷 1341-1353页

作者： Herzberg, William Rowe, Daniel B. Hauptmann, Andreas Hamilton, Sarah J. Marquette Univ Dept Math & Stat Sci Milwaukee WI 53233 USA Univ Oulu Res Unit Math Sci Oulu 90570 Finland UCL Dept Comp Sci London WC1E 6BT England

The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we present a flexible framework to extend model-based learning directly to nonuniform meshes, by interpreting the mesh as a graph and formulating our network architectures using graph convolutional neural networks. This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh. We present results for Electrical Impedance Tomography, a severely ill-posed nonlinear inverse problem that is frequently solved via optimization-based methods, where the forward problem is solved by finite element methods. Results for absolute EIT imaging are compared to standard iterative methods as well as a graph residual network. We show that the GCNM has good generalizability to different domain shapes andmeshes, out of distribution data as well as experimental data, from purely simulated training data and without transfer training.

关键词： Finite element method graph convolutional networks model-based deep learning conductivity electrical impedance tomography

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DA-MUSIC: Data-Driven DoA Estimation via deep Augmented MUSIC Algorithm

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第2期73卷 2771-2785页

作者： Merkofer, Julian P. Revach, Guy Shlezinger, Nir Routtenberg, Tirza van Sloun, Ruud J. G. Swiss Fed Inst Technol CH-8092 Zurich Switzerland Eindhoven Univ Technol Elect Engn Dept NL-5612 AZ Eindhoven Netherlands Swiss Fed Inst Technol D ITET CH-8092 Zurich Switzerland Ben Gurion Univ Negev Sch ECE IL-8499000 Beer Sheva Israel Princeton Univ ECE Dept Princeton NJ 08540 USA

Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice. MUSIC is a model-based algorithm, relying on an accurate mathematical description of the relationship between the signals and the measurements and assumptions on the signals themselves (non-coherent, narrowband sources). As such, it is sensitive to model imperfections. In this work, we propose to overcome these limitations of MUSIC by augmenting the algorithm with specifically designed neural architectures. Our proposed deep augmented MUSIC (DA-MUSIC) algorithm is thus a hybrid model-based/data-driven DoA estimator, which leverages data to improve performance and robustness while preserving the interpretable flow of the classic method. DA-MUSIC is shown to learn to overcome limitations of the purely model-based method, such as its inability to successfully localize coherent sources as well as estimate the number of coherent signal sources present. We further demonstrate the superior resolution of the DA-MUSIC algorithm in synthetic narrowband and broadband scenarios as well as with real-world data of DoA estimation from seismic signals.

关键词： DoA estimation MUSIC model-based deep learning

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LoRD-Net: Unfolded deep Detection Network With Low-Resolution Receivers

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5651-5664页

作者： Khobahi, Shahin Shlezinger, Nir Soltanalian, Mojtaba Eldar, Yonina C. Univ Illinois ECE Dept Chicago IL 60607 USA Ben Gurion Univ Negev Sch ECE IL-84105 Beer Sheva Israel Weizmann Inst Sci Fac Math & CS IL-7610001 Rehovot Israel

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on the extreme case of one-bit quantizers, and propose a deep detector entitled LoRD-Net for recovering information symbols from one-bit measurements. Our method is a model-aware data-driven architecture based on deep unfolding of first-order optimization iterations. LoRD-Net has a task-based architecture dedicated to recovering the underlying signal of interest from the one-bit noisy measurements without requiring prior knowledge of the channel matrix through which the one-bit measurements are obtained. The proposed deep detector has much fewer parameters compared to black-box deep networks due to the incorporation of domain-knowledge in the design of its architecture, allowing it to operate in a data-driven fashion while benefiting from the flexibility, versatility, and reliability of model-based optimization methods. LoRD-Net operates in a blind fashion, which requires addressing both the non-linear nature of the data-acquisition system as well as identifying a proper optimization objective for signal recovery. Accordingly, we propose a two-stage training method for LoRD-Net, in which the first stage is dedicated to identifying the proper form of the optimization process to unfold, while the latter trains the resulting model in an end-to-end manner. We numerically evaluate the proposed receiver architecture for one-bit signal recovery in wireless communications and demonstrate that the proposed hybrid methodology outperforms both data-driven and model-based state-of-the-art methods, while utilizing small datasets, on the order of merely similar to 500 samples, for training.

关键词： Receivers Task analysis Computational modeling Training Quantization (signal) Maximum likelihood estimation Detectors model-based deep learning deep unfolding machine learning massive MIMO low-resolution signal processing one-bit quantization

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