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Integration of Physics-Based and Data-Driven Models for Hyperspectral image Unmixing: A summary of current methods

IEEE signal processing MAGAZINE 2023年第2期40卷 61-74页

作者： Chen, Jie Zhao, Min Wang, Xiuheng Richard, Cedric Rahardja, Susanto Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Nice Sophia Antipolis Nice France Univ Michigan Ann Arbor MI USA Northwestern Polytech Univ Xian 710072 Peoples R China Univ Cote Azur F-06000 Nice France Singapore Inst Technol Singapore Singapore

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task, physics-based methods have become popular because, with their explicit mixing models, they can provide a clear interpretation. Nevertheless, because of their limited modeling capabilities, especially when analyzing real scenes with unknown complex physical properties, these methods may not be accurate. On the other hand, data-driven methods using deep learning in particular have developed rapidly in recent years, thanks to their superior capability in modeling complex nonlinear systems. Simply transferring these methods as black boxes to perform unmixing may lead to low interpretability and poor generalization ability. To bring together the best of two worlds, recent research efforts have focused on combining the advantages of both physics-based models and data-driven methods. In this article, we present an overview of recent advances on this topic from various perspectives, including deep neural network (DNN) design, prior capturing, and loss selection. We summarize these methods within a common optimization framework and discuss ways of enhancing our understanding of these methods. The related source codes are made publicly available at http://***/xiuheng-wang/awesome-hyperspectral-image-unmixing.

关键词： Deep learning Analytical models Source coding neural networks Closed box Task analysis Nonlinear systems

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Fine-Grained image Generation Network With Radar Range Profiles Using Cross-Modal Visual Supervision

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2024年第2期72卷 1339-1352页

作者： Bao, Jiacheng Li, Da Li, Shiyong Zhao, Guoqiang Sun, Houjun Zhang, Yi Beijing Inst Technol Sch Integrated Circuits & Elect Beijing Key Lab Millimeter Wave & Terahertz Tech Beijing 100081 Peoples R China

Electromagnetic imaging methods mainly utilize converted sampling, dimensional transformation, and coherent processing to obtain spatial images of targets, which often suffer from accuracy and efficiency problems. Deep neural network (DNN)-based high-resolution imaging methods have achieved impressive results in improving resolution and reducing computational costs. However, previous works exploit single modality information from electromagnetic data;thus, the performances are limited. In this article, we propose an electromagnetic image generation network (EMIG-Net), which translates electromagnetic data of multiview 1-D range profiles (1DRPs), directly into bird-view 2-D high-resolution images under cross-modal supervision. We construct an adversarial generative framework with visual images as supervision to significantly improve the imaging accuracy. Moreover, the network structure is carefully designed to optimize computational efficiency. Experiments on self-built synthetic data and experimental data in the anechoic chamber show that our network has the ability to generate high-resolution images, whose visual quality is superior to that of traditional imaging methods and DNN-based methods, while consuming less computational cost. Compared with the backprojection (BP) algorithm, the EMIG-Net gains a significant improvement in entropy (72%), peak signal-to-noise ratio (PSNR;150%), and structural similarity (SSIM;153%). Our work shows the broad prospects of deep learning in radar data representation and high-resolution imaging and provides a path for researching electromagnetic imaging based on learning theory.

关键词： Cross-modal supervision deep neural network (DNN) electromagnetic imaging generative adversarial network (GAN) radar range profile

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Localized Binarization of Document images Based on Suprathreshold stochastic Resonance 4

Localized Binarization of Document Images Based on Suprathre...

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4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yan, Xiaoyue Mu, Dazhong School of Information Engineering Beijing Institute Of Graphic Communication Beijing China

ISBN: (纸本)9798331528850

When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes an image binarization method based on stochastic resonance theory. First, we divide the image into sub-blocks and set a binarization threshold based on the statistical properties of the pixels in each sub-block. Next, the image signal is converted into a one-dimensional time series signal using Hilbert scanning. The processed signals are input into a threshold array system, which amplifies the weak edge information in the input signals through the stochastic resonance phenomenon. Subsequently, we performed modulation and inverse scanning on the output signals of the system to generate the binary image for each sub-block. Finally, all sub-block binary images were combined to complete the binarization of the overall image. Experimental results show that the method proposed in this paper can effectively retain the detailed information of document images and significantly outperforms the traditional binarization method regarding image quality. © 2024 IEEE.

关键词： stochastic systems

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Convolutional neural Network Algorithm and Application Method for Real-Time Beam Steering in RF System

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IEEE ACCESS 2024年 12卷 134498-134509页

作者： Byun, Sung-June Ann, Da-Yeong Jo, Jong-Wan Lee, Heejeong Jasmine Jung, Yeon-Jae Kim, Seok-Kee Pu, Young-Gun Lee, Kang-Yoon Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea SKAIChips Suwon 16571 South Korea Sungkyunkwan Univ Coll Informat & Commun Engn Suwon 16419 South Korea

This paper presents a novel artificial intelligence (AI)-based phase shift system in a beamforming system implemented with field programmable gate array (FPGA)-based hardware by integrating a conventional convolutional neural network (CNN) algorithm. The position of the target can be determined through a phase shifter in a beamforming system using artificial intelligence. In a system that emits a beam from a radio frequency (RF) transmitter and receives a beam from an RF receiver, artificial intelligence can control the phase. It controls the phase of the transmitter for beam scanning and the phase to optimize the signal-to-noise ratio (SNR) of the receiver. The position of the target was detected by learning the signal input data from the receiver. Targets were detected through two-beam scanning processes in a 3D space. The first is a coarse process of detecting the approximate position of the target in the entire space, and the second is a fine process of detecting the area in detail after detecting the first approximate position. The phases of the individual antennae should be controlled for optimal beamforming based on the 5x 5 antenna, and the phase is detected at high speed by holding the phase large in the first coarse tuning. The second scan entails a narrow range scan with a small phase to detect it at a high speed accurately. This study shows that with FPGA, AI beamforming can be implemented through two scanning methods without image sensors. Based on the receiver's 5x5 antenna, the CNN input feature consisted of 35x35 classifies the class with high accuracy.

关键词： Artificial intelligence Array signal processing Field programmable gate arrays Antennas Radio frequency Receiving antennas Transceivers Convolutional neural networks Convolutional neural network artificial intelligence beamforming beamforming algorithm RF system

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Spatiotemporal features representation with dynamic mode decomposition for hand gesture recognition using deep neural networks

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signal image AND VIDEO processing 2024年第4期18卷 3745-3759页

作者： Sharma, Bhavana Panda, Jeebananda DTU Dept Elect & Commun Engn New Delhi 110042 India

Hand Gesture Recognition (HGR) with complexity and diversity of hand images in uncontrolled environment is a challenging task because of complex backgrounds, light illumination, strong occlusions, blur motion. This work provides a thorough examination of spatiotemporal feature extraction with deep learning model in order to overcome practical variations in lighting and fluctuations of physical hand's movement in both space and time. The hand skin color is first filtered through YCbCr color space and in order to train the hand images, MediaPipe is used to distinguish the specific gesture region. With respect to spatial variations, the spatiotemporal features extraction is done by Dynamic Mode Decomposition (DMD) technique, where hand key features are decoupled with time dynamics and modes in order to obtain time-frequency analysis. Thus, the received reconstructed signal has an enhanced visibility of skin-color pixels. The extensive experiment is demonstrated by deep neural network ResNet18 for better classification on three publicly available datasets, namely, Ego hand dataset, American Sign Language (ASL) dataset and Senz3D dataset. This work outplays existing state-of-arts methods remarkable regarding spatiotemporal features extraction with an accuracy of Ego hand dataset is 97.85% and ASL dataset is 98.49% at specific dynamic modes three, whereas Senz3D dataset achieves 98.51% classification accuracy at dynamic mode two. We have obtained a competitive outcome when comparing the State-Of-The-Art (SOTA) techniques available for HGR.

关键词： Hand gesture recognition Dynamic mode decomposition (DMD) Time dynamics Spatiotemporal features Deep neural network

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NERF-GAZE: A HEAD-EYE REDIRECTION PARAMETRIC MODEL FOR GAZE ESTIMATION 49

NERF-GAZE: A HEAD-EYE REDIRECTION PARAMETRIC MODEL FOR GAZE ...

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49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yin, Pengwei Wang, Jingjing Dai, Jiawu Wu, Xiaojun Hikvis Res Inst Hangzhou Peoples R China Harbin Inst Technol Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Gaze estimation is a fundamental aspect of many visual tasks. However, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on neural Radiance Field. This model allows for dense gaze data generation with view consistency and accurate gaze direction. Furthermore, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, which enables us to separately control the attributes of the face, identity, illumination, and eye gaze direction. As a result, diverse 3D-aware gaze datasets can be obtained by manipulating the latent code belonging to different face attributes in an unsupervised manner. Our method has achieved state-of-the-art performance in image quality and accuracy gaze annotations compared with existing gaze data synthesis methods. Extensive experiments on several benchmarks demonstrate that our method can effectively improve domain generalization and domain adaptation in the gaze estimation task.

关键词： gaze estimation neural radiance field continuous image generation

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Deep Residual and Classified neural Networks for Inverse Halftoning

Deep Residual and Classified Neural Networks for Inverse Hal...

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Asia-Pacific-signal-and-Information-processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Guo, Jing-Ming Sankarasrinivasan, S. Let Viet Hung Liu, Wei Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10607 Taiwan Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9798350300673

Inverse Halftoning is an ill-posed problem which restores a continuous-tone image from a halftone image. Many conventional inverse halftoning methods have tried to solve this problem, yet the recovered images still suffer several unwanted artifacts and fine details losses. In addition, recent deep neural network-based approaches have shown their advantages on restoration of the high-quality images with rich textures and detailed information. However, it is truly challenging for these deep learning methods to reconstruct a variety of different halftone patterns. For instance, the model trained with the halftone patterns of homogenous distribution cannot perform ideally for high structural information patterns. To solve this problem, an inverse halftoning based on deep residual neural network (DRNN) and variance classification is proposed. The proposed method utilizes benefits of progressive learning concept involving two main stages: First, the DRNN extracts numerous intrinsic features of an image, and significantly removes the halftone patterns. Subsequently, consecutive deep residual blocks are integrated to network restoring the fine details with good accuracy. Consequently, the proposed model comprises the integration of various DRNNs which are trained over various statistical ranges with respect to the statistics of halftone patches. Comprehensive experimental results demonstrate that the proposed deep learning-based technique significantly outperforms not only the conventional methods but also deep learning approaches.

关键词： Inverse halftoning progressive learning residual neural networks variance classification convolutional neural network

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MAS-EGNN: A Multi-Agent EGNN-based Spectrum Allocation Optimization Framework for V2X Communications

MAS-EGNN: A Multi-Agent EGNN-based Spectrum Allocation Optim...

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9th International Conference on signal and image processing (ICSIP)

作者： He, Yingxue Peng, Li Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9798350350920

With the development of V2X technology, efficient spectrum resource management is critical to ensure the reliability and overall system performance of vehicle-to-vehicle communications. Traditional spectrum allocation methods often do not take into account inter-vehicle interference. In this paper, we introduce an innovative approach to eliminate interference in vehicle-to-vehicle communication, the MAS-EGNN framework. Initially, an Equivariant Graph neural Networks (EGNN) is utilized to dynamically update the graph representation through node and edge conditions to effectively capture the relationships and dependencies between vehicles. Subsequently, multi-intelligence reinforcement learning techniques allow multiple intelligences to interact simultaneously within the environment, with each independently adapting to changes in the surrounding environment to optimize overall network performance. The effectiveness of the approach in improving communication quality and system throughput is verified through the simulation of V2X communication scenarios and the implementation of corresponding optimization strategies. The experimental results show that the method significantly reduces interference and optimizes V2X spectrum allocation compared with the traditional spectrum allocation strategy.

关键词： V2X Communication Spectrum Resource Management Equivariant Graph neural Networks Multi-Agent Reinforcement Learning

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NERD: neural FIELD-BASED DEMOSAICKING 30

NERD: NEURAL FIELD-BASED DEMOSAICKING

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30th IEEE International Conference on image processing (ICIP)

作者： Kerepecky, Tomas Sroubek, Filip Novozamsky, Adam Flusser, Jan Czech Acad Sci Inst Informat Theory & Automat Prague Czech Republic Czech Tech Univ Fac Nucl Sci & Phys Engn Prague Czech Republic

ISBN: (纸本)9781728198354

We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by representing an image as a coordinate-based neural network with sine activation functions. The inputs to the network are spatial coordinates and a low-resolution Bayer pattern, while the outputs are the corresponding RGB values. An encoder network, which is a blend of ResNet and U-net, enhances the implicit neural representation of the image to improve its quality and ensure spatial consistency through prior learning. Our experimental results demonstrate that NeRD outperforms traditional and state-of-the-art CNN-based methods and significantly closes the gap to transformer-based methods.

关键词： Demosaicking neural field implicit neural representation

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Radar active oppressive interference suppression based on generative adversarial network

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IET RADAR SONAR AND NAVIGATION 2024年第7期18卷 1193-1202页

作者： Yu, Yongzhi You, Yu Wang, Ping Guo, Limin Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin Peoples R China York Univ Dept Elect Engn & Comp Sci Toronto ON Canada

Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short-time Fourier transform time-varying filtering, short-time fractional Fourier transform time-varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms. An intelligent interference suppression method based on deep learning is proposed. Its interference suppression performance and robustness are better than the existing methods. image

关键词： GANs interference (signal) learning (artificial intelligence) radar signal processing

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