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30th IEEE International Conference on Image Processing (ICIP)

作者： Takanami, Keigo Bandoh, Yukihiro Takamura, Seishi Tanaka, Yuichi Tokyo Univ Agr & Technol Tokyo Japan Osaka Univ Osaka Japan NTT Corp Yokosuka Kanagawa Japan Hosei Univ Tokyo Japan

ISBN: (纸本)9781728198354

We propose a simultaneous method of multimodal graph signal denoising and graph learning. Since sensor networks distributed in space can capture multiple modalities of data, referred to as modalities, they are assumed to have an underlying structure or correlations both in space and modality. Such multimodal data are regarded as graph signals on a twofold graph. Like regular signals, multimodal graph signals can be corrupted by noise during their sensing process. Furthermore, their spatial/modality relationships are not given a priori: We need to estimate twofold graphs during denoising. In this paper, we propose a signal denoising method on twofold graphs where graphs are learned simultaneously. Specifically, we formulate an optimization problem for that, and an iterative algorithm for solving it is unrolled with deep algorithm unrolling (DAU). In the proposed method, the parameters in iterations are learned from training data that results in faster convergence and denoising quality improvements. Experimental results demonstrate that the proposed method outperforms existing graph signal denoising methods.

关键词： Multimodal data signal denoising graph learning deep algorithm unrolling

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Graph Signal Restoration Using Nested deep algorithm unrolling

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3296-3311页

作者： Nagahama, Masatoshi Yamada, Koki Tanaka, Yuichi Chan, Stanley H. Eldar, Yonina C. Tokyo Univ Agr & Technol Dept Elect Engn & Comp Sci Koganei Tokyo 1848588 Japan Japan Sci & Technol Agcy PRESTO Kawaguchi Saitama 3320012 Japan Tokyo Univ Sci Dept Elect Engn Tokyo 1258585 Japan Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Weizmann Inst Sci Fac Math & Comp Sci IL-7610001 Rehovot Israel

Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then suggest a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second approach, the unrolled ADMM-based denoiser is incorporated as a submodule, leading to a nested DAU structure. The parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Our approach is interpretable and keeps the number of parameters small since we only tune graph-independent regularization parameters. We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements over several existing techniques in terms of root mean squared error in both tasks.

关键词： Signal processing algorithms Image restoration Signal restoration Noise reduction Task analysis Symmetric matrices Optimization Graph signal processing signal restoration deep algorithm unrolling Plug-and-Play ADMM

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Restoration of Time-Varying Graph Signals using deep algorithm unrolling 48

Restoration of Time-Varying Graph Signals using Deep Algorit...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kojima, Hayate Noguchi, Hikari Yamada, Koki Tanaka, Yuichi Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Japan Tokyo University of Science Department of Electrical Engineering Japan Osaka University Graduate School of Engineering Japan

ISBN: (纸本)9781728163277

In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. deep algorithm unrolling is a method that learns parameters in an iterative optimization algorithm with deep learning techniques. It is expected to improve convergence speed and accuracy while the iterative steps are still interpretable. In the proposed method, the minimization problem is formulated so that the time-varying graph signal is smooth both in time and spatial domains. The internal parameters, i.e., time domain FIR filters and regularization parameters, are learned from training data. Experimental results using synthetic data and real sea surface temperature data show that the proposed method improves signal reconstruction accuracy compared to several existing time-varying graph signal re- construction methods. © 2023 IEEE.

关键词： deep algorithm unrolling graph signal processing Time-varying graph signals

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Image reconstruction of multispectral sparse sampling photoacoustic tomography based on deep algorithm unrolling

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PHOTOACOUSTICS 2024年 38卷 100618页

作者： Ge, Jia Mo, Zongxin Zhang, Shuangyang Zhang, Xiaoming Zhong, Yutian Liang, Zhaoyong Hu, Chaobin Chen, Wufan Qi, Li Southern Med Univ Sch Biomed Engn 1023 Shatai Rd Guangzhou 510515 Guangdong Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Image Proc 1023 Shatai Rd Guangzhou 510515 Guangdong Peoples R China Southern Med Univ Guangdong Prov Engn Lab Med Imaging & Diagnost Tec 1023 Shatai Rd Guangzhou 510515 Guangdong Peoples R China

Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue in vivo. Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior. Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor. Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods. We firstly provide a thorough analysis of DAU for PAT reconstruction. Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem. Experimental results on numerical simulation, in vivo animal imaging, and multispectral unmixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.

关键词： Multispectral photoacoustic tomography deep algorithm unrolling PnP-ADMM Image reconstruction

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MULTIMODAL GRAPH SIGNAL DENOISING VIA TWOFOLD GRAPH SMOOTHNESS REGULARIZATION WITH deep algorithm unrolling 47

MULTIMODAL GRAPH SIGNAL DENOISING VIA TWOFOLD GRAPH SMOOTHNE...

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

作者： Nagahama, Masatoshi Tanaka, Yuichi Tokyo Univ Agr & Technol Tokyo Japan Japan Sci & Technol Agcy PRESTO Saitama Japan

ISBN: (纸本)9781665405409

We propose a denoising method of multimodal graph signals with twofold smoothness regularization. Graph signal processing assumes that a signal has an underlying structure that is represented by a graph. In each node of the graph, we often have multimodal data or features that are correlated across modalities. Since these multimodal data are measured by various sensors, the observed data will be noisy. In this paper, we assume that a multimodal signal is smooth on two underlying graphs: One is a spatial graph (i.e., relationship among nodes) and the other is a modality graph (i.e., relationship among modalities). We formulate a regularized minimization problem based on smoothness on the twofold graphs. The problem is solved with an alternating minimization scheme. To avoid a hand-crafted parameter tuning that is usually costly and converges to local minima, we utilize deep algorithm unrolling (DAU) to train the parameters in the algorithm. To validate the proposed method, we conduct experiments on synthetic data and demonstrate that our method outperforms various existing graph signal denoising methods.

关键词： Graph signal processing deep algorithm unrolling signal denoising

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Graph Signal Restoration Using Nested deep algorithm unrolling

Graph Signal Restoration Using Nested Deep Algorithm Unrolli...

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

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GRAPH SIGNAL DENOISING USING NESTED-STRUCTURED deep algorithm unrolling

GRAPH SIGNAL DENOISING USING NESTED-STRUCTURED DEEP ALGORITH...

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

作者： Nagahama, Masatoshi Yamada, Koki Tanaka, Yuichi Chan, Stanley H. Eldar, Yonina C. Tokyo Univ Agr & Technol Tokyo Japan Purdue Univ W Lafayette IN 47907 USA Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781728176055

In this paper, we propose a deep algorithm unrolling (DAU) based on a variant of the alternating direction method of multiplier (ADMM) called Plug-and-Play ADMM(PnP-ADMM) for denoising of signals on graphs. DAU is a trainable deep architecture realized by unrolling iterations of an existing optimization algorithm which contains trainable parameters at each layer. We also propose a nested-structured DAU: Its submodules in the unrolled iterations are also designed by DAU. Several experiments for graph signal denoising are performed on synthetic signals on a community graph and U.S. temperature data to validate the proposed approach. Our proposed method outperforms alternative optimization- and deep learning-based approaches.

关键词： Graph signal processing optimization algorithm deep learning signal denoising deep algorithm unrolling

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