Point cloud is a critically important geometric data structure, and researchers have increasingly focused on and achieved promising results in terms of point cloud processing since PointNet's pioneering work. However, most previous methods only represent the shape of point clouds through coordinates or normal vectors, neglecting the intrinsic geometric and topological properties of this data structure. In this paper, we present an effective point cloud analysis approach which is using topological information. By employing a simplified version of the PointNet++(SSG version), we conduct benchmark experiments on the ModelNet40 dataset to evaluate TPA's performance in the classification task. Our improved method can still directly process point clouds, as the topological invariants ensure the permutation invariance of the input points. Simulation results show that the topological approach based on persistent homology can effectively provide topological structural features and improve the accuracy of the models.
{"title":"An efficient 3D point cloud classification approach via persistent homology","authors":"Xin-Yu Zhou, Yu Pan, Lei Zhang, Huafei Sun*","doi":"10.1117/12.3032040","DOIUrl":"https://doi.org/10.1117/12.3032040","url":null,"abstract":"Point cloud is a critically important geometric data structure, and researchers have increasingly focused on and achieved promising results in terms of point cloud processing since PointNet's pioneering work. However, most previous methods only represent the shape of point clouds through coordinates or normal vectors, neglecting the intrinsic geometric and topological properties of this data structure. In this paper, we present an effective point cloud analysis approach which is using topological information. By employing a simplified version of the PointNet++(SSG version), we conduct benchmark experiments on the ModelNet40 dataset to evaluate TPA's performance in the classification task. Our improved method can still directly process point clouds, as the topological invariants ensure the permutation invariance of the input points. Simulation results show that the topological approach based on persistent homology can effectively provide topological structural features and improve the accuracy of the models.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile Edge Computing (MEC) has emerged as a pivotal technology to meet the increasing demands of mobile applications. However, in high-dynamic MEC environments, load balancing and performance optimization among servers remain challenging. Focusing on server load balancing in task offloading in MEC environment. It constructs a framework for ultra-dense network environments and formulates the problem of computation offloading and resource allocation as a Markov Decision Process (MDP). Subsequently, a learning algorithm based on Proximal Policy Optimization (PPO) is proposed to reduce load standard deviation, achieve load balancing, and simultaneously minimize the system's total delay energy consumption, thereby enhancing the efficiency of the MEC system. Simulation results demonstrate that, compared to random offloading strategies, all-offloading strategies, and the Deep Deterministic Policy Gradient algorithm, the algorithm proposed consistently demonstrates superior performance in load balancing across varying numbers of users and task sizes.
{"title":"Research on offloading strategies for mobile edge computing in ultradense networks","authors":"Ruobin Wang, Lijun Li, Meiling Li, Wenhua Gao, Zengshou Dong","doi":"10.1117/12.3032060","DOIUrl":"https://doi.org/10.1117/12.3032060","url":null,"abstract":"Mobile Edge Computing (MEC) has emerged as a pivotal technology to meet the increasing demands of mobile applications. However, in high-dynamic MEC environments, load balancing and performance optimization among servers remain challenging. Focusing on server load balancing in task offloading in MEC environment. It constructs a framework for ultra-dense network environments and formulates the problem of computation offloading and resource allocation as a Markov Decision Process (MDP). Subsequently, a learning algorithm based on Proximal Policy Optimization (PPO) is proposed to reduce load standard deviation, achieve load balancing, and simultaneously minimize the system's total delay energy consumption, thereby enhancing the efficiency of the MEC system. Simulation results demonstrate that, compared to random offloading strategies, all-offloading strategies, and the Deep Deterministic Policy Gradient algorithm, the algorithm proposed consistently demonstrates superior performance in load balancing across varying numbers of users and task sizes.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in machine learning have made forged video and audio more convincing. This poses a threat to the security of individuals, societies and nations. To address this threat, the ASVspoof initiative was conceived to spearhead research on Automatic Speaker Verification (ASV) for anti-spoofing. Currently, most research on ASVspoof has focused on detecting whether speech has been tampered with. However, little attention has been paid to the recognition of speech forgery algorithms. Moreover, in the real world, new forgery algorithms keep emerging, making it difficult to adapt forgery algorithm recognition models trained under closed-set conditions to realistic open-set scenarios. Therefore, we propose a method based on prototype learning and adaptive thresholding for recognizing speech forgery algorithms in open-set. The method uses manifold mixup and dummy prototypes to simulate and recognize unknown speech forgery algorithms. Prototype classification improves the ability to recognize speech forgery algorithms with high similarity. At the same time, it has the advantage of preventing catastrophic forgetting and facilitates subsequent incremental training using samples of newly recognized forgery algorithms. Thus, our method increases the number of recognized categories for forgery algorithms. Experimental results show that our method is effective. The codes are available at https://github.com/multimedia-security/open-set-recognization.
{"title":"Towards recognition of open-set speech forgery algorithms by using prototype learning","authors":"Zuxing Zhao, Haiyan Zhang, Hai Min, Yanxiang Chen","doi":"10.1117/12.3031938","DOIUrl":"https://doi.org/10.1117/12.3031938","url":null,"abstract":"Recent advances in machine learning have made forged video and audio more convincing. This poses a threat to the security of individuals, societies and nations. To address this threat, the ASVspoof initiative was conceived to spearhead research on Automatic Speaker Verification (ASV) for anti-spoofing. Currently, most research on ASVspoof has focused on detecting whether speech has been tampered with. However, little attention has been paid to the recognition of speech forgery algorithms. Moreover, in the real world, new forgery algorithms keep emerging, making it difficult to adapt forgery algorithm recognition models trained under closed-set conditions to realistic open-set scenarios. Therefore, we propose a method based on prototype learning and adaptive thresholding for recognizing speech forgery algorithms in open-set. The method uses manifold mixup and dummy prototypes to simulate and recognize unknown speech forgery algorithms. Prototype classification improves the ability to recognize speech forgery algorithms with high similarity. At the same time, it has the advantage of preventing catastrophic forgetting and facilitates subsequent incremental training using samples of newly recognized forgery algorithms. Thus, our method increases the number of recognized categories for forgery algorithms. Experimental results show that our method is effective. The codes are available at https://github.com/multimedia-security/open-set-recognization.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To solve the problem of coherent sound source direction of arrival (DOA) estimation of microphone uniform circular array in indoor reverberation environment, an improved MUSIC algorithm for microphone uniform circular array (UCA) is proposed. The pre-processing uses the mode space to change into several virtual uniform linear arrays. The maximum feature vector matrix is constructed by decomposing the covariance matrix of the snapshot data. The information of all sound sources is used, and the covariance matrix is also restored to the diagonal matrix, which greatly reduces the error caused by the pre-processing. The spatial spectral function is obtained, and the spectral function is searched to obtain N directions and pitch angles corresponding to N signals. The MATLAB tool is used to simulate the indoor reverberation environment microphone uniform ring array model and the improved MUSIC algorithm. The simulation results show that the algorithm has better estimation accuracy for indoor sound sources under ideal conditions, and has higher resolution and lower signal-to-noise ratio.
为解决室内混响环境下传声器均匀圆形阵列的相干声源到达方向（DOA）估计问题，提出了一种改进的传声器均匀圆形阵列（UCA）MUSIC 算法。预处理利用模式空间转换为多个虚拟均匀线性阵列。通过分解快照数据的协方差矩阵来构建最大特征向量矩阵。利用所有声源的信息，同时将协方差矩阵还原为对角矩阵，大大减少了预处理带来的误差。获得空间谱函数，并搜索谱函数以获得 N 个信号对应的 N 个方向和俯仰角。利用 MATLAB 工具对室内混响环境麦克风均匀环形阵模型和改进的 MUSIC 算法进行仿真。仿真结果表明，该算法在理想条件下对室内声源的估计精度更高，分辨率更高，信噪比更低。
{"title":"DOA estimation based on mode and maximum eigenvector algorithm with reverberation environment","authors":"Hengyi Liu, Zhenghong Liu, Linxia Su, Liyan Luo","doi":"10.1117/12.3032086","DOIUrl":"https://doi.org/10.1117/12.3032086","url":null,"abstract":"To solve the problem of coherent sound source direction of arrival (DOA) estimation of microphone uniform circular array in indoor reverberation environment, an improved MUSIC algorithm for microphone uniform circular array (UCA) is proposed. The pre-processing uses the mode space to change into several virtual uniform linear arrays. The maximum feature vector matrix is constructed by decomposing the covariance matrix of the snapshot data. The information of all sound sources is used, and the covariance matrix is also restored to the diagonal matrix, which greatly reduces the error caused by the pre-processing. The spatial spectral function is obtained, and the spectral function is searched to obtain N directions and pitch angles corresponding to N signals. The MATLAB tool is used to simulate the indoor reverberation environment microphone uniform ring array model and the improved MUSIC algorithm. The simulation results show that the algorithm has better estimation accuracy for indoor sound sources under ideal conditions, and has higher resolution and lower signal-to-noise ratio.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the widespread application of deep learning frameworks, large-scale computing and GPU programming are receiving increased attention. For upper-layer applications that utilize GPUs for computational communication, such as TensorFlow and PyTorch, improving the communication efficiency of the underlying communication library is of paramount importance to enhance the overall performance of the frameworks. Among them, the RCCL (Rocm Collective Communication Library) GPU communication library, provided by the Rocm (Radeon Open Compute platform) computing platform, supports various collective communication operations and point-to-point operations. Through analysis, we have identified a problem in the initialization and usage of the ring channel network in the RCCL library, specifically in multi-network card systems. This issue results in certain network cards being unable to communicate, leading to wasted system resources. To address this problem, optimizations can be made at the code level by introducing data structures and algorithms to control the invocation of network cards. The goal is to adjust the usage strategy of multiple network cards in the ring channel network without modifying the original design concept of RCCL. After optimization, extensive evaluations were conducted using a large-scale GPU cluster. The optimized RCCL library achieved significant improvements in communication performance. Under a communication scale of 16 compute nodes and 64 GPUs, the peak bandwidth increased from 5.28GB/s to 7.78GB/s. In inter-node collective communication tests, the performance improvement reached up to 60%. The improved RCCL library provides better low-level communication performance for upper-layer applications on the Rocm computing platform, offering enhanced communication support.
{"title":"Network communication optimization of RCCL communication library in Multi-NIC systems","authors":"Shuaiming He, Wei Wan, Junhong Li","doi":"10.1117/12.3031956","DOIUrl":"https://doi.org/10.1117/12.3031956","url":null,"abstract":"With the widespread application of deep learning frameworks, large-scale computing and GPU programming are receiving increased attention. For upper-layer applications that utilize GPUs for computational communication, such as TensorFlow and PyTorch, improving the communication efficiency of the underlying communication library is of paramount importance to enhance the overall performance of the frameworks. Among them, the RCCL (Rocm Collective Communication Library) GPU communication library, provided by the Rocm (Radeon Open Compute platform) computing platform, supports various collective communication operations and point-to-point operations. Through analysis, we have identified a problem in the initialization and usage of the ring channel network in the RCCL library, specifically in multi-network card systems. This issue results in certain network cards being unable to communicate, leading to wasted system resources. To address this problem, optimizations can be made at the code level by introducing data structures and algorithms to control the invocation of network cards. The goal is to adjust the usage strategy of multiple network cards in the ring channel network without modifying the original design concept of RCCL. After optimization, extensive evaluations were conducted using a large-scale GPU cluster. The optimized RCCL library achieved significant improvements in communication performance. Under a communication scale of 16 compute nodes and 64 GPUs, the peak bandwidth increased from 5.28GB/s to 7.78GB/s. In inter-node collective communication tests, the performance improvement reached up to 60%. The improved RCCL library provides better low-level communication performance for upper-layer applications on the Rocm computing platform, offering enhanced communication support.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In marine ship ad-hoc networks, the distance between ships is usually several kilometers. The IEEE802.11 standard designed for indoor networks is not applicable so that the use of directional antennas extends the communication range. In this case, the node cannot listen to data transmission activity outside the beam. And the asymmetric gain of the antenna can lead to hidden termination problems. This paper proposes a directional MAC protocol for ship ad-hoc networks (DMSA) by using Automatic Identification System (AIS) to obtain ship node position information. The transmitting node will send polling RTS (P-RTS) to the surrounding nodes before sending data, and the neighboring nodes around it will create a P-NAV table to record the direction of the data transmission after hearing the P-RTS frame. After completing the RTS/CTS handshake, power control is used to solve the hidden terminal problem caused by antenna gain asymmetry
{"title":"A directional MAC protocol for marine ship ad-hoc networks","authors":"Xiaoran Yang, Shengming Jiang","doi":"10.1117/12.3032021","DOIUrl":"https://doi.org/10.1117/12.3032021","url":null,"abstract":"In marine ship ad-hoc networks, the distance between ships is usually several kilometers. The IEEE802.11 standard designed for indoor networks is not applicable so that the use of directional antennas extends the communication range. In this case, the node cannot listen to data transmission activity outside the beam. And the asymmetric gain of the antenna can lead to hidden termination problems. This paper proposes a directional MAC protocol for ship ad-hoc networks (DMSA) by using Automatic Identification System (AIS) to obtain ship node position information. The transmitting node will send polling RTS (P-RTS) to the surrounding nodes before sending data, and the neighboring nodes around it will create a P-NAV table to record the direction of the data transmission after hearing the P-RTS frame. After completing the RTS/CTS handshake, power control is used to solve the hidden terminal problem caused by antenna gain asymmetry","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yongheng Zhao, Xuzhao Chai, Cuicui He, Yiming Lu, Pengwei Wen, Li Yan, Zhao Li
In this work, the method of Model Predictive Control (MPC) and Improved Whale Optimization Algorithm (IWOA) has been proposed to solve multiple unmanned aerial vehicles (UAVs) tracking a moving target in urban environment. The problem models are established, including the UAV model, target model, environment model and cost function model. Adopting MPC as a control framework for UAV target tracking, WOA is chosen as the solver of MPC. To further improve the optimized efficiency, the introduced strategies include bootstrap initialization strategy, double-difference variational strategy, adaptive weighting strategy and elite selection strategy. The compared experiments show the control method in this paper has better tracking performance and is a reliable technique for UAV tracking the moving target.
{"title":"Multi-UAV tracking target in urban environments by model predictive control and improved whale optimizer","authors":"Yongheng Zhao, Xuzhao Chai, Cuicui He, Yiming Lu, Pengwei Wen, Li Yan, Zhao Li","doi":"10.1117/12.3032000","DOIUrl":"https://doi.org/10.1117/12.3032000","url":null,"abstract":"In this work, the method of Model Predictive Control (MPC) and Improved Whale Optimization Algorithm (IWOA) has been proposed to solve multiple unmanned aerial vehicles (UAVs) tracking a moving target in urban environment. The problem models are established, including the UAV model, target model, environment model and cost function model. Adopting MPC as a control framework for UAV target tracking, WOA is chosen as the solver of MPC. To further improve the optimized efficiency, the introduced strategies include bootstrap initialization strategy, double-difference variational strategy, adaptive weighting strategy and elite selection strategy. The compared experiments show the control method in this paper has better tracking performance and is a reliable technique for UAV tracking the moving target.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to challenges such as the large number of parameters and high computational demands of vehicle appearance damage detection models, which hinder deployment on mobile devices, this paper presents a study focusing on lightweight and high-precision optimization of the YOLOv5s target detection algorithm. Specifically, we introduce the lightweight network into the YOLOv5s architecture to create a more efficient network. Furthermore, we integrate the attention mechanism to enhance feature extraction capabilities and employ knowledge distillation to improve algorithm accuracy. These enhancements aim to boost target detection performance. The experimental results illustrate that our optimized YOLOv5 algorithm achieves significant improvements in both speed and accuracy on the car damage dataset.
{"title":"A lightweight car damage detection algorithm","authors":"Qishan Pei, Xinkuang Wang, Zhongcheng Wu","doi":"10.1117/12.3031904","DOIUrl":"https://doi.org/10.1117/12.3031904","url":null,"abstract":"In response to challenges such as the large number of parameters and high computational demands of vehicle appearance damage detection models, which hinder deployment on mobile devices, this paper presents a study focusing on lightweight and high-precision optimization of the YOLOv5s target detection algorithm. Specifically, we introduce the lightweight network into the YOLOv5s architecture to create a more efficient network. Furthermore, we integrate the attention mechanism to enhance feature extraction capabilities and employ knowledge distillation to improve algorithm accuracy. These enhancements aim to boost target detection performance. The experimental results illustrate that our optimized YOLOv5 algorithm achieves significant improvements in both speed and accuracy on the car damage dataset.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Silicon modulators, which play a crucial role in silicon photonics systems, are currently trending towards lower biases and improved bandwidth. The plasma dispersion effect of silicon modulators highlights the importance of carrier concentration in improving performance. Common doping profiles have been optimized for high efficiency but may suffer from increased loss. Our horizontal S-shaped modulator improves silicon modulators with excellent VπL of 0.77 V·cm and low loss of 10.9 dB/cm, with small resistance and capacitance enhancing bandwidth over 27 GHz. This design is suitable for high-speed and low-voltage applications with benefits of saving power.
{"title":"High-efficiency silicon modulator of horizontal S-shaped profile","authors":"Zijian Zhu, Ying Zhao, Fuwan Gan","doi":"10.1117/12.3031892","DOIUrl":"https://doi.org/10.1117/12.3031892","url":null,"abstract":"Silicon modulators, which play a crucial role in silicon photonics systems, are currently trending towards lower biases and improved bandwidth. The plasma dispersion effect of silicon modulators highlights the importance of carrier concentration in improving performance. Common doping profiles have been optimized for high efficiency but may suffer from increased loss. Our horizontal S-shaped modulator improves silicon modulators with excellent VπL of 0.77 V·cm and low loss of 10.9 dB/cm, with small resistance and capacitance enhancing bandwidth over 27 GHz. This design is suitable for high-speed and low-voltage applications with benefits of saving power.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image denoising algorithm based on depth learning generally uses convolution sparse self-coding network as the main framework of the denoising network. However, although convolution sparse self-coding network can effectively suppress the noise information in the image, it has the problem of loss of certain details in the image after denoising. Aiming at this defect, on the basis of convolutional sparse self-encoding network, the detail information of each layer feature map is extracted from the output of each encoder layer using self-attention mechanism, and the detail information is integrated into the input layer of the corresponding decoder using residual connection method. Experimental results show that compared with the traditional convolutional self-coding noise reduction network, the proposed convolutional self-coding network based on self-attention residuals can effectively improve the level of network noise reduction. At the same time, compared with the mainstream noise reduction network, the proposed algorithm can also achieve better noise reduction effect.
{"title":"Image denoising algorithm based on self-attention residual network","authors":"Wei Wu, Hao Wu","doi":"10.1117/12.3031897","DOIUrl":"https://doi.org/10.1117/12.3031897","url":null,"abstract":"Image denoising algorithm based on depth learning generally uses convolution sparse self-coding network as the main framework of the denoising network. However, although convolution sparse self-coding network can effectively suppress the noise information in the image, it has the problem of loss of certain details in the image after denoising. Aiming at this defect, on the basis of convolutional sparse self-encoding network, the detail information of each layer feature map is extracted from the output of each encoder layer using self-attention mechanism, and the detail information is integrated into the input layer of the corresponding decoder using residual connection method. Experimental results show that compared with the traditional convolutional self-coding noise reduction network, the proposed convolutional self-coding network based on self-attention residuals can effectively improve the level of network noise reduction. At the same time, compared with the mainstream noise reduction network, the proposed algorithm can also achieve better noise reduction effect.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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