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Subsampling Decomposition based k-Space Refinement for Accelerated MRI Reconstruction

Subsampling Decomposition based k-Space Refinement for Accel...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaoyu Qiao Weisheng Li Bin Xiao Yuping Huang Lijian Yang Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In accelerated MRI reconstruction problem, directly recovering all the missing k-space data from undersampled measurements is highly ill-posed and often leads to suboptimal performance. To address the problem, we propose a novel deep unfolding network (DUN) with subsampling decomposition (SD) based k-space refinement to mitigate the ill-posedness. Our method employs a parallel network architecture with a primary branch unfolded by gradient descent-inspired optimization process (GD-PB) for reconstruction. Additionally, we introduce an SD-based auxiliary branch (SD-AB) that decompose the inverse problem into moderately corrupted subproblems. We design a novel subsampling mask predictor that captures both global and local spatial correlations in k-space, ensuring the SD-AB effectively preserves the most well-reconstructed subsets as reliable region (RR). The RR in SD-AB is used to periodically refine the intermediate outputs of the GD-PB, achieving improved accuracy. Experimental results reveal that our method significantly outperforms conventional and SD-based DUN techniques, achieving superior PSNR and SSIM results compared with cutting-edge methods.

关键词： Correlation Inverse problems Magnetic resonance imaging Signal processing Network architecture Reliability engineering Sensors Speech processing image reconstruction Optimization

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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Some Intriguing Observations on the Learnt Matrices in Deep Unfolded Networks

Some Intriguing Observations on the Learnt Matrices in Deep ...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Kartheek Kumar Reddy Nareddy Inbasekaran Perumal Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bengaluru Institute of Data Science German Aerospace Center Jena Germany Department of Electronics and Communication Engineering National Institute of Technology Mangaluru Karnataka

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep-unfolded networks (DUNs) have set new performance benchmarks in fields such as compressed sensing, image restoration, and wireless communications. DUNs are built from conventional iterative algorithms, where an iteration is transformed into a layer/block of a network with learnable parameters. Despite their huge success, the reasons behind their superior performance over their iterative counterparts are not fully understood. This paper focuses on enhancing the explainability of DUNs by investigating potential reasons behind their superior performance over traditional iterative methods. We concentrate on the Learnt Iterative Shrinkage-Thresholding Algorithm (LISTA), a foundational contribution that achieves sparse recovery with significantly fewer layers than its iterative counterpart, ISTA. Our findings reveal that the learnt matrices in LISTA always have Gaussian distributed entries regardless of whether the sensing matrix is random Gaussian, Bernoulli, exponential, or uniform. The findings also show that the singular values of the learnt matrices exceed unity, despite which, the reconstruction scheme is stable. We conjecture that the activation function may have a role to play in ensuring stability. We also present an unbiasing technique that substantially improves the sparse recovery performance by reestimating the amplitudes based on the converged support.

关键词： Training Wireless communication Accuracy Superresolution Signal processing algorithms Gaussian distribution Iterative algorithms Sparse matrices image reconstruction Convergence

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G-Litter Marine Litter Dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration

G-Litter Marine Litter Dataset Augmentation with Diffusion M...

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33rd Euromicro International conference on parallel, distributed, and Network-Based processing, PDP 2025

作者： Mellone, Gennaro De Vita, Ciro Giuseppe Nardo, Emanuel Di Coviello, Giuseppe Luccio, Diana Di Aucelli, Pietro Patrizio Ciro Ciaramella, Angelo Montella, Raffaele University of Napoli Parthenope Department of Science and Technology Napoli Italy University of Naples Parthenope Dep. of Science and Technologies Naples Italy Integrated System NEC Laboratories America Inc PrincetonNJ United States

ISBN: (纸本)9798331524937

Marine litter detection is crucial for environmental monitoring, yet the imbalance in existing datasets limits model performance in identifying various types of waste accurately. This paper presents an efficient data augmentation pipeline that combines generative diffusion models (e.g., Stable Diffusion) and Large Language Models (LLMs) to expand the G-Litter dataset, a marine litter dataset designed for autonomous detection in heterogeneous environments. Leveraging scalable diffusion models for image generation and Alpaca LLMs for diverse prompt generation, our approach augments underrepresented classes by generating over 200 additional images per class, significantly improving the dataset's balance. Training G-Litter augmented dataset using YOLOv8 for object detection demonstrated an increase in detection performance, improving recall by 7.82% and mAP50 by 3.87% (compared with baseline results). This study emphasizes the potential for combining generative AI with HPC resources to automate data augmentation on large-scale, unstructured datasets, particularly in edge computing contexts for real-time marine monitoring. The models were tested on real videos captured during simulated missions, demonstrating a superior ability to detect submerged objects in dynamic scenarios. These results highlight the potential of generative AI techniques to improve dataset quality and detection model performance, laying the foundation for further expansion in real-time marine monitoring. © 2025 IEEE.

关键词： image enhancement

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FPGA-based Real-Time High-resolution image processing for rocket on-board processing 17

FPGA-based Real-Time High-resolution Image Processing for ro...

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17th IEEE International conference on Computer Research and Development, ICCRD 2025

作者： Zhou, Hui Wang, Xiaofeng Xie, Yujia Cao, Yongxiang Tang, Yu Huo, Yongchun Wen, Huan Zhao, Xiongbo Li, Chaoran National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China Beihang University Computer Science and Engineering Beijing China Beihang University National Superior College for Engineers Beijing China

ISBN: (纸本)9798331531881

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-Time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-The-Art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-To-end computation time for

关键词： image enhancement

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JANE: Joint Angle Networks Assisting 3D Human Pose Estimation

JANE: Joint Angle Networks Assisting 3D Human Pose Estimatio...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jinhuan Wang Yuzhen Zhao Xujie Song Wenzhou Chen Qi Xuan Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology HangZhou China Department of Computer Science and Technology Hangzhou Dianzi University HangZhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D human pose estimation (HPE) is crucial due to its extensive applications. While current 3D HPE methods focus on human skeleton topology for accuracy, they often overlook joint angle information, which is vital in 2D-to-3D pose lifting. This paper introduces the Joint Angle Network (JANE) model to leverage joint angle features from 2D poses. We propose an angle graph convolution module to extract joint angle features and a multi-stage parallel fusion module to integrate these features with input image data. Experiments on Human3.6M show that our method improves accuracy by 8.7% and 2.6% over non-spatiotemporal baselines using 2D ground truth and 2D pose detectors, respectively.

关键词： Visualization Solid modeling Three-dimensional displays Accuracy Convolution Pose estimation Feature extraction Transformers Topology Speech processing

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Regularized Deep Convolutional Encoding-Decoding Generative Network for Remote Sensing Cloud image Compression

Regularized Deep Convolutional Encoding-Decoding Generative ...

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Data Compression conference (DCC)

作者： Yu Wang Jing Yang Sijia Wang Xingli Yang The School of Modern Educational Technology Shanxi University Taiyuan China The School of Mathematics and Statistics Shanxi University Taiyuan China The School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Typically, meteorological cloud observations are often presented in the form of high-quality and high-resolution images, such as satellite remote sensing cloud images and ground-based remote sensing cloud images. However, faced with such a large number of high-resolution images, even for the existing GPU servers and parallel computing with high-speed data processing capabilities, the computational cost of image recognition and analysis is very large, or even unacceptable. A widely used method is to resize the original image to a version with a decreased resolution and to obtain cloud information from this decreased image. Clearly, image compression is crucial. Universal interpolation based image compression methods compress the image size by directly clipping unimportant pixels at the pixel level, which may fail to capture important image feature information such as image structure, resulting in low image recognition performance. In particular, the popular deep encoding-decoding generative network can perfectly reconstruct the image almost identical to the original image by self-supervised learning the useful high-resolution image feature representation. Thus, in this paper, a remote sensing cloud image compression method based on regularized deep convolutional encoding-decoding generation network is proposed by constraining encoding-decoding network and the interpolation method. The proposed method extracts useful feature representations from the original image through an encoding process with a convolutional neural network model, and then reconstructs the compressed image at a specified compression ratio through a decoding process with a deconvolutional neural network model. In the proposed method, a Kullback-Leibler (KL) divergence constraint is introduced to align the feature representation distribution with a standard normal distribution, enhancing the accuracy of the feature representation. Additionally, the regularization constraint between the feature maps o

关键词： Interpolation image coding image resolution image recognition Feature extraction Computational efficiency Servers Remote sensing image reconstruction Standards

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A Dual-Stream Network with Non-Stationary Characteristics-Enhanced for SST image Prediction

A Dual-Stream Network with Non-Stationary Characteristics-En...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Min Ye Jiahao Du Ning Song Xinyue Liang Bohan Li Jie Nie Fac. of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sea surface temperature (SST) prediction is crucial for understanding global climate and marine ecosystems, and its anomalies can lead to extreme weather events. SST exhibits complex non-stationary over natural spatio-temporal processes. However, most of the existing deep learning methods for SST prediction only extract non-stationary features through the simple state transitions of classic CNNs or RNNs, which are too simplistic to capture higher-order non-stationary trends in complex SST sequences. Therefore, we propose a DSNet_SST network, aiming to enhance the extraction of non-stationary information from the spatio-temporal SST evolution. It incorporates two parallel modules: one for capturing high-order temporal non-stationarity based on stacked memory in memory (MIM) blocks and the other one for extracting spatial correlation non-stationarity by multiscale difference operation. A third module adaptively integrates these features to improve the accuracy and stability of SST prediction. The experimental results, relying on the OISST data obtained from both remote sensing satellites and in situ platforms, demonstrate the advantages of the proposed DSNet_SST over baseline methods in terms of SST prediction accuracy.

关键词： Temperature sensors Accuracy Signal processing Feature extraction Spatiotemporal phenomena Data mining Speech processing Thermal stability Surface treatment Meteorology

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parallel and distributed methods for image processing IV

Parallel and distributed methods for image processing IV

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2000年

作者： Hongchi Shi Patrick C. Coffield Divyendu Sinha

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Cloud-Enhanced Transfer Learning Framework for Medical image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare 3

Cloud-Enhanced Transfer Learning Framework for Medical Image...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Prabu, S. Chandra, N. Subhash Bhavani, M. Varma, V. K. Chaitanya Soni, Mukesh Mahendra Institute of Technology Department of Electronics and Communication Engineering Namakkal India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalle India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sagi Rama Krishnam Raju Engineering College Department of Mechanical Engineering Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331508456

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals. © 2025 IEEE.

关键词： distributed cloud

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