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image denoising based on Swin Transformer Residual Conv U-Net

Image denoising based on Swin Transformer Residual Conv U-Ne...

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Yong Gan Shaohui Zhou Haonan Chen Yuefeng Wang Zhengzhou University of Technology Zhengzhou China College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at $\sigma=15,33.87$ and 0.915 at $\sigma=25$, and 28.96 and 0.810 at $\sigma=50$.

关键词： Training Visualization AWGN Computational modeling Noise reduction Transformers Data augmentation

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A Multimodal Fusion Framework for Fake News Detection via Multi-Attention Mechanism

A Multimodal Fusion Framework for Fake News Detection via Mu...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Yongxin Yu Yanqiang Li Ke Ji Zhenxiang Chen Kun Ma Xiaofan Zhao School of Information Science and Engineering University of Jinan Jinan China School of Information and Cyber Security People’s Public Security University of China Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Social media platforms have emerged as the primary channels for the general public to access and share information. However, the rapid dissemination of news has led to the emergence of a significant amount of unverified content, posing a serious threat to media credibility and network security. The current solutions primarily focus on detecting fake news by extracting and fusing features from images and text, but they have not fully utilized the relevance within and between modalities, resulting in suboptimal fusion effects. In this paper, we propose a multimodal fusion framework via multi-attention mechanism (MFMA), which considers not only the semantic and relevance features of images, textual content, and Optical Character Recognition (OCR) text from the news, but also the multimodal relevance features between them. First, we extract text within images (OCR text) to address the issue of inadequate utilization of the visual modality in traditional methods. Second, we select more robust feature extractors to capture the independent characteristics of each modality and use a pre-trained FcaNet model to extract more comprehensive image quality features. Additionally, two relevance extraction modules are designed to achieve feature fusion within and between modalities. Finally, the combined features are fed into a classifier to determine the authenticity of the news. The experimental results and analysis indicate that the model we proposed effectively enhances the performance of fake news detection.

关键词： image quality Visualization distributed processing Social networking (online) Optical character recognition Semantics Network security Feature extraction Data mining Fake news

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Virtual prototyping of complex optical systems on multiprocessor workstations

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OPTICAL ENGINEERING 2023年第2期62卷 021006-021006页

作者： Zhdanov, Andrey Zhdanov, Dmitry Sorokin, Maksim Potemin, Igor ITMO Univ St Petersburg Russia

Nowadays, optical designers often use multiprocessor workstations for the virtual prototyping of complex optical systems. Modern workstations may have several CPUs with up to 128 virtual cores each and a non-uniform access speed to different memory areas. Effective implementation of virtual prototyping methods and algorithms requires the development of special methods for efficient algorithm parallelization and shared memory access. As a basis for the virtual prototyping, we proposed a progressive backward photon mapping method that allows for reducing the amount of data used by photon maps, speeding up the luminance calculation process, and estimating the luminance errors for the resulting image. The main algorithmic complexity of this method is the need to synchronize data when calculating and accumulating the luminance of indirect and caustic illumination. The authors propose the three-level semi-synchronous parallelization method, which consists of fully synchronous, semi-synchronous, and asynchronous levels with illumination processing effectively distributed among the computation threads. The main benefit of the developed method is that it does not require additional synchronization when accumulating luminance, thus increasing the image synthesis speed. The designed three-level method can also be used in distributed systems with good scalability. The results obtained with virtual prototypes of complex optical systems are presented.(c) The Authors.

关键词： virtual prototyping ray tracing parallel computing realistic rendering backward photon mapping multiprocessor workstations

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Implementation and Optimization of 8×8 Block Discrete Cosine Transform on MGPUSim

Implementation and Optimization of 8×8 Block Discrete Cosin...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Shuang Yang Yaobin Wang Ling Li Jiawei Qin Guotang Bi School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Discrete cosine transform for 8×8 block(DCT8×8) is widely used in image compression due to its high signal decorrelation rate. Current research for DCT is mainly focused on CPU and single GPU platforms, and the exploration of multi-GPU architectures is still insufficient. With the rapid increase of image data volume, the DCT8×8 algorithm on CPU and single GPU architectures can no longer meet the demand of efficient computation. Therefore, optimizing DCT8×8 algorithms on multi-GPU architectures becomes particularly important. To address this challenge, this paper explores the porting and optimization strategies of DCT algorithms on the new MGPUSim ***, we extend the instruction implementation of MG-PUSim to accomplish the porting of the original DCT8×8 algorithm (O-DCT), and then propose a kernel implementation strategy for optimizing the DCT8×8 algorithm by setting the size of the workgroup appropriately so that the kernel function can take advantage of the symmetry of the transformation matrix to reduce the redundant computation, which we refer to as the M-DCT. We conducted experiments on multiple grayscale images with different resolutions. The experimental results show that O-DCT performs well on MGPUSim, achieving an average 1.93× speedup ratio compared to the original DCT8×8 kernel (kernel1) in the CUDA SDK. Further experimental results show that M-DCT improves the execution efficiency on MGPUSim by 1.76× relative to O-DCT, and also achieves an average of 1.3× acceleration ratio compared to the optimized DCT8×8 kernel (kernel2) in the CUDA SDK, which improves the computational performance of the DCT8×8 algorithm on multi-GPU architectures. It provides new ideas and methods for future research on image compression on multi-GPU architectures.

关键词： image resolution image coding Instruction sets Graphics processing units Computer architecture Gray-scale Computational efficiency Discrete cosine transforms Kernel Optimization

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RAMMM: A Rapid Attention-Based Multimodal Modification Model for Massive image Generation

RAMMM: A Rapid Attention-Based Multimodal Modification Model...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Zhenyuan Xu Xuewen Chen Taotao Wang Wei Quan Yi Zhu Nanjing Audit University Nanjing China

Nowadays, deep learning (DL) techniques have found extensive applications in the Internet of Things (IoT) community, such as autonomous driving and medical diagnosis. Despite these successful implementations, elevated data collection expenses, data confidentiality concerns, and privacy issues all contribute to the complexity and costliness of data acquisition in DL-based image processing. image modification methods based on diffusion models can subtly modify images to generate new images with similar content to the original image but with unique differences, providing a powerful tool for data augmentation. Nonetheless, diffusion models exhibit constraints in cross-modal image modification, such as sensitivity to prompt, model complexity, and user-friendliness. To address these issues, this paper proposes a rapid attention-based multimodal modification model (RAMMM) to facilitate straightforward and efficient text-guided image modification. Our proposed RAMMM primarily enhances the text processing and image generation procedures via an attention mechanism, thus improving the ability in capturing semantic and contextual information within the text. Consequently, RAMMM excels in generating high-quality sample images aligned with the description provided for the modified text. In addition, by utilizing text-guided image modification, RAMMM is capable of generating batches of image samples systematically to augment the dataset size. Evaluation results demonstrate that RAMMM can improve the performance and generalization capabilities of diffusion models by enhancing both the quality and quantity of the dataset. When employed for data augmentation on the CIFAR10 dataset, RAMMM achieves a 1.5% increase in target recognition accuracy.

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Scaling Single-image Super-Resolution Training on Modern HPC Clusters: Early Experiences

Scaling Single-Image Super-Resolution Training on Modern HPC...

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35th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Anthony, Quentin Xu, Lang Subramoni, Hari Panda, Dhabaleswar K. Dk Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781665435772

Deep Learning (DL) models for super-resolution (DLSR) are an emerging trend in response to the growth of ML/DL applications requiring high-resolution images. DLSR methods have also shown promise in domains such as medical imaging, surveillance, and microscopy. However, DLSR models are extremely computationally demanding, and require unreasonably long training times on modern Volta GPUs. In our experiments, we observed only 10.3 images/second on a single Volta GPU for training EDSR, a state-of-the-art DLSR model for single-image super-resolution. In comparison, a Volta GPU can process 360 images/second while training ResNet-50, a state-of-the-art model for image classification. Therefore, we believe supercomputers provide a good candidate to speed up DLSR model training. In this paper, we select EDSR as the representative DLSR PyTorch model. Further, we introduce Horovod-based distributed EDSR training. However, we observed poor default EDSR scaling performance on the Lassen HPC system at Lawrence Livermore National Laboratory. To investigate the performance degradations, we perform exhaustive communication profiling. These profiling insights are then used to optimize CUDA-Aware MPI for DLSR models by ensuring advanced MPI designs involving CUDA IPC and registration caching are properly applied by DL frameworks. We present a comprehensive scaling study of EDSR with MVAPICH2-GDR and NCCL up to 512 GPUs on Lassen. We demonstrate an improvement in scaling efficiency by 15.6% over default Horovod training, which translates to a 1.26x speedup in training performance.

关键词： DNN Training Performance Characterization MVAPICH2 MPI PyTorch Horovod Super Resolution

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Industrial CT Truncated Projection Data Reconstruction System Based on Artificial Intelligence

Industrial CT Truncated Projection Data Reconstruction Syste...

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Signal processing and Intelligent Computing (SPIC), International Conference on

作者： Yang Yang Songjie Wu Liaoning Institute of Science and Technology Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In industry, Computer Tomography (CT) is a key technology used for non-destructive testing and evaluation of internal structures, but significant challenges remain in the processing of truncated projection data. The aim of this study is to investigate an artificial intelligence-based reconstruction system for industrial CT truncated projection data. We investigate the common data truncation problem in industrial CT technology and propose a new data reconstruction method. The method uses the ideas of full variance minimization and segmentation constants to achieve accurate reconstruction of truncated projection data by means of parallel computation. Our study validates the effectiveness and superiority of the method through comparative experiments. Compared with conventional methods, our system excels in both accuracy and stability of reconstruction results, and is better able to handle the challenges posed by truncated data. In addition, we explore the practical application value of the system, demonstrating its potential use in industrial CT. From a single-core to a dual-core processor, the processing time is reduced from 120 to 60 seconds, showing a doubling of the processing speed, which indicates that tasks are efficiently distributed and executed among multiple cores. The research results in this paper will have a positive impact on the development and application of industrial CT technology and promote its wide application in industrial production and quality control.

关键词： Technological innovation image segmentation Accuracy TV Computed tomography parallel processing Minimization Stability analysis image reconstruction Testing

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Research on parallel Detection Technology of Remote Sensing Object Based on Deep Learning 4

Research on Parallel Detection Technology of Remote Sensing ...

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4th International Conference on Intelligent Autonomous Systems (ICoIAS)

作者： Zhang, Chengguang Zhang, Xuebo Jiang, Min Space Engn Univ Sch Grad Beijing Peoples R China Space Engn Univ Sch Space Informat Beijing Peoples R China

ISBN: (纸本)9781665441957

In recent years, methods based on deep learning have become a hot spot and trend in the field of remote sensing object detection, and a series of encouraging results have been achieved. With the development of science and technology, people's technology and ability to acquire remote sensing data have been comprehensively improved, and high-resolution large-scale remote sensing image data has increased dramatically. However, the current mainstream object detection models cannot directly input large-scale high-resolution images for prediction. This paper proposes a remote sensing object parallel detection algorithm, which uses the mpi4py module to realize multi-CPU+GPU distributed parallel processing. Based on the yolov4 object detection model, it can detect remote sensing images of any scale without reducing the prediction accuracy. And shorten the object detection time according to the number of distributed nodes. The experimental results show that the parallel algorithm has a high speedup ratio, and the parallel detection technology has a good development prospect in the field of remote sensing image object detection.

关键词： remote sensing object detention deep learning parallel processing

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Efficiently Classify Synthesized Facial images Generated By Different Synthesis methods 12

Efficiently Classify Synthesized Facial Images Generated By ...

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12th International Conference on Graphics and image processing (ICGIP)

作者： Li, Wei Qiao, Peng Dou, Yong Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9781510642782;9781510642775

With image generation and manipulation as one of impressive progress of convolutional neural networks (CNNs), facial image synthesis methods, e.g., DeepFakes, pose serious challenges to social and personal security. Specifically, we find that (1) CNN-based synthesized facial image detection methods generally fail to identify synthesized images generated by other synthesis methods;(2) classical detection methods exploiting one-class support vector machines (SVMs) and traditional features of video clips fail when only one image is available. In view of the above challenges, we propose and experimentally verify a method combining CNNs features and one-class SVMs, which not only effectively detects synthesized facial images generated by different methods, but also has good robustness to the variances of the scene content.

关键词： synthesized facial image detection convolutional neural networks one-class SVMs robustness

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MonkeyGPT: Generative AI in Network Anomaly Detection of Video Conference Applications

MonkeyGPT: Generative AI in Network Anomaly Detection of Vid...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Junhao Xu Dongbiao He Chen Zhang Xu Zhou Zhongxing Ming Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Sangfor Technologies Inc Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The rapid advancement of generative artificial intelligence (GAI) has led to the creation of transformative applications such as ChatGPT, which significantly boosts text processing efficiency and diversifies audio, image, and video content. Beyond digital content creation, GAI’s capability to analyze complex data distributions holds immense potential for next-generation networks and communications, especially given the swift rise of video conferencing applications (VCAs). This paper presents a dynamic, real-time method for detecting anomalous network links in video conferencing applications. The proposed tool, MonkeyGPT, generates tracing representations of network activity and trains a large language model from scratch to serve as a detection system based on network traffic data. Unlike traditional methods, MonkeyGPT provides an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a wider range of anomalies. We demonstrate the effectiveness of MonkeyGPT as an anomaly detection tool in real-world VCAs. The results indicate that the model possesses strong detection capabilities, achieving an accuracy rate of over 97%. It is applicable to various platforms, including Zoom, Microsoft Teams, Tencent Meeting, and Feishu, showcasing its robust adaptability.

关键词： Accuracy Generative AI Large language models Telecommunication traffic Streaming media Real-time systems Data models Anomaly detection Videoconferences Text processing

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