Journal of King Saud University-Computer and Information Sciences最新文献_第2页

Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach 耦合相移 STAR-RIS 网络中的高级安全措施：DRL 方法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-09 DOI: 10.1016/j.jksuci.2024.102215

Abdul Wahid , Syed Zain Ul Abideen , Manzoor Ahmed , Wali Ullah Khan , Muhammad Sheraz , Teong Chee Chuah , Ying Loong Lee

{"title":"Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach","authors":"Abdul Wahid , Syed Zain Ul Abideen , Manzoor Ahmed , Wali Ullah Khan , Muhammad Sheraz , Teong Chee Chuah , Ying Loong Lee","doi":"10.1016/j.jksuci.2024.102215","DOIUrl":"10.1016/j.jksuci.2024.102215","url":null,"abstract":"<div><div>The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology, offering full-space coverage by manipulating electromagnetic wave propagation. However, the inherent flexibility of STAR-RIS introduces new vulnerabilities, making secure communication a significant challenge. To overcome these challenges, we propose a deep reinforcement learning (DRL) based secure and efficient beamforming optimization strategy, leveraging the deep deterministic policy gradient (DDPG) algorithm. By framing the problem as a Markov decision process (MDP), our approach enables the DDPG algorithm to learn optimal strategies for beamforming and transmission and reflection coefficients (TARCs) configurations. This method is specifically designed to optimize phase-shift coefficients within the STAR-RIS environment, effectively managing the coupled phase shifts and complex interactions that are critical for enhancing physical layer security (PLS). Through extensive simulations, we demonstrate that our DRL-based strategy not only outperforms traditional optimization techniques but also achieves real-time adaptive optimization, significantly improving both confidentiality and network efficiency. This research addresses key gaps in secure wireless communication and sets a new standard for future advancements in intelligent, adaptable network technologies.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Endoscopic video aided identification method for gastric area 内窥镜视频辅助胃区识别方法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102208

Xiangwei Zheng, Dejian Su, Xuanchi Chen, Mingzhe Zhang

{"title":"Endoscopic video aided identification method for gastric area","authors":"Xiangwei Zheng, Dejian Su, Xuanchi Chen, Mingzhe Zhang","doi":"10.1016/j.jksuci.2024.102208","DOIUrl":"10.1016/j.jksuci.2024.102208","url":null,"abstract":"<div><div>Probe-based confocal laser endomicroscopy (pCLE) is a significant diagnostic instrument and is frequently utilized to diagnose the severity of gastric intestinal metaplasia (GIM). The physicians must comprehensively analyze video clips recorded with pCLE from the gastric antrum, gastric body, and gastric angle area to determine the patient’s condition. However, due to the limitations of the pCLE’s microscopic imaging structure, the gastric areas detected cannot be identified and recorded in real time, which may poses a risk of missing potential areas of disease occurrence and is not conducive to the subsequent precise treatment of the lesion area. Therefore, this paper proposes an endoscopic video aided identification method for identifying gastric areas (EVIGA), which are utilized for determining the detected areas of pCLE in real-time. Firstly, the start time of the diagnosis clip is determined by real-time detecting the working states of pCLE. Then, the endoscopic video clip is truncated according to the correspondence between pCLE and endoscopic video in the time sequence for detecting gastric areas. In order to accurately identify pCLE detected gastric areas, a probe-based confocal laser endomicroscopy diagnosis area identification model (pCLEDAM) is constructed with an hourglass convolution designed for single-frame feature extraction and a temporal feature-sensitive extraction structure for spatial feature extraction. The extracted feature maps are unfolded and fed into the fully connected layer to classify the detected areas. To validate the proposed method, 67 clinical confocal laser endomicroscopy diagnosis cases are collected from a tertiary care hospital, and 500 video clips are finally reserved after audited for dataset construction. Experiments show that the accuracy of area identification on the test dataset achieves 96.0% and is much higher than other related algorithms, achieving the accurate identification of pCLE detected areas.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On-chain zero-knowledge machine learning: An overview and comparison 链上零知识机器学习：概述与比较

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102207

Vid Keršič, Sašo Karakatič, Muhamed Turkanović

{"title":"On-chain zero-knowledge machine learning: An overview and comparison","authors":"Vid Keršič, Sašo Karakatič, Muhamed Turkanović","doi":"10.1016/j.jksuci.2024.102207","DOIUrl":"10.1016/j.jksuci.2024.102207","url":null,"abstract":"<div><div>Zero-knowledge proofs introduce a mechanism to prove that certain computations were performed without revealing any underlying information and are used commonly in blockchain-based decentralized apps (dapps). This cryptographic technique addresses trust issues prevalent in blockchain applications, and has now been adapted for machine learning (ML) services, known as Zero-Knowledge Machine Learning (ZKML). By leveraging the distributed nature of blockchains, this approach enhances the trustworthiness of ML deployments, and opens up new possibilities for privacy-preserving and robust ML applications within dapps. This paper provides a comprehensive overview of the ZKML process and its critical components for verifying ML services on-chain. Furthermore, this paper explores how blockchain technology and smart contracts can offer verifiable, trustless proof that a specific ML model has been used correctly to perform inference, all without relying on a single trusted entity. Additionally, the paper compares and reviews existing frameworks for implementing ZKML in dapps, serving as a reference point for researchers interested in this emerging field.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IPSRM: An intent perceived sequential recommendation model IPSRM：意图感知顺序推荐模型

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102206

Chaoran Wang , Mingyang Wang , Xianjie Wang , Yingchun Tan

{"title":"IPSRM: An intent perceived sequential recommendation model","authors":"Chaoran Wang , Mingyang Wang , Xianjie Wang , Yingchun Tan","doi":"10.1016/j.jksuci.2024.102206","DOIUrl":"10.1016/j.jksuci.2024.102206","url":null,"abstract":"<div><h3>Objectives:</h3><div>Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process.</div></div><div><h3>Methods:</h3><div>This paper introduces an intent perceived sequential recommendation model (IPSRM). IPSRM employs the generalized expectation–maximization (EM) framework, alternating between learning sequence representations and optimizing the model to better capture the underlying intentions of user interactions. Specifically, IPSRM maps unlabeled behavioral sequences into frequency domain filtering and random Gaussian distribution space. These mappings reduce the impact of noise and improve the learning of user behavior representations. Through clustering process, IPSRM captures users’ potential interaction intentions and incorporates them as one of the supervisions into the contrastive self-supervised learning process to guide the optimization process.</div></div><div><h3>Results:</h3><div>Experimental results on four standard datasets demonstrate the superiority of IPSRM. Comparative experiments also verify that IPSRM exhibits strong robustness under cold start and noisy interaction conditions.</div></div><div><h3>Conclusions:</h3><div>Capturing latent user intentions, integrating intention-based supervision into model optimization, and mitigating noise in sequential modeling significantly enhance the performance of sequential recommendation systems.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring 用于肿瘤生物标记物评分的全切片图像实时分割和分类

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102204

Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan

{"title":"Real-time segmentation and classification of whole-slide images for tumor biomarker scoring","authors":"Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan","doi":"10.1016/j.jksuci.2024.102204","DOIUrl":"10.1016/j.jksuci.2024.102204","url":null,"abstract":"<div><div>Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-stream dynamic graph structure network for document-level relation extraction 用于文档级关系提取的双流动态图结构网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-03 DOI: 10.1016/j.jksuci.2024.102202

Yu Zhong, Bo Shen

{"title":"Dual-stream dynamic graph structure network for document-level relation extraction","authors":"Yu Zhong, Bo Shen","doi":"10.1016/j.jksuci.2024.102202","DOIUrl":"10.1016/j.jksuci.2024.102202","url":null,"abstract":"<div><div>Extracting structured information from unstructured text is crucial for knowledge management and utilization, which is the goal of document-level relation extraction. Existing graph-based methods face issues with information confusion and integration, limiting the reasoning capabilities of the model. To tackle this problem, a dual-stream dynamic graph structural network is proposed to model documents from various perspectives. Leveraging the richness of document information, a static document heterogeneous graph is constructed. A dynamic heterogeneous document graph is then induced based on this foundation to facilitate global information aggregation for entity representation learning. Additionally, the static document graph is decomposed into multi-level static semantic graphs, and multi-layer dynamic semantic graphs are further induced, explicitly segregating information from different levels. Information from different streams is effectively integrated via an information integrator. To mitigate the interference of noise during the reasoning process, a noise regularization mechanism is also designed. The experimental results on three extensively utilized publicly accessible datasets for document-level relation extraction demonstrate that our model achieves F1 scores of 62.56%, 71.1%, and 86.9% on the DocRED, CDR, and GDA datasets, respectively, significantly outperforming the baselines. Further analysis also demonstrates the effectiveness of the model in multi-entity scenarios.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation ParaU-Net：用于肺结节分割的改进型 UNet 并行编码网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102203

Yingqi Lu , Xiangsuo Fan , Jinfeng Wang , Shaojun Chen , Jie Meng

{"title":"ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation","authors":"Yingqi Lu , Xiangsuo Fan , Jinfeng Wang , Shaojun Chen , Jie Meng","doi":"10.1016/j.jksuci.2024.102203","DOIUrl":"10.1016/j.jksuci.2024.102203","url":null,"abstract":"<div><div>Accurate segmentation of lung nodules is crucial for the early detection of lung cancer and other pulmonary diseases. Traditional segmentation methods face several challenges, such as the overlap between nodules and surrounding anatomical structures like blood vessels and bronchi, as well as the variability in nodule size and shape, which complicates the segmentation algorithms. Existing methods often inadequately address these issues, highlighting the need for a more effective solution. To address these challenges, this paper proposes an improved multi-scale parallel fusion encoding network, ParaU-Net. ParaU-Net enhances the segmentation accuracy and model performance by optimizing the encoding process, improving feature extraction, preserving down-sampling information, and expanding the receptive field. Specifically, the multi-scale parallel fusion mechanism introduced in ParaU-Net better captures the fine features of nodules and reduces interference from other structures. Experiments conducted on the LIDC (The Lung Image Database Consortium) public dataset demonstrate the excellent performance of ParaU-Net in segmentation tasks, with results showing an IoU of 87.15%, Dice of 92.16%, F1-score of 92.24%, F2-score of 92.33%, and F0.5-score of 92.69%. These results significantly outperform other advanced segmentation methods, validating the effectiveness and accuracy of the proposed model in lung nodule CT image analysis. The code is available at <span><span>https://github.com/XiaoBai-Lyq/ParaU-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification LungNeXt：利用增强型 Mel 光谱图进行肺音分类的新型轻量级网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102200

Fan Wang , Xiaochen Yuan , Yue Liu , Chan-Tong Lam

{"title":"LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification","authors":"Fan Wang , Xiaochen Yuan , Yue Liu , Chan-Tong Lam","doi":"10.1016/j.jksuci.2024.102200","DOIUrl":"10.1016/j.jksuci.2024.102200","url":null,"abstract":"<div><div>Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-throughput systolic array-based accelerator for hybrid transformer-CNN networks 基于高通量收缩阵列的混合变压器-网络加速器

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102194

Qingzeng Song , Yao Dai , Hao Lu , Guanghao Jin

{"title":"High-throughput systolic array-based accelerator for hybrid transformer-CNN networks","authors":"Qingzeng Song , Yao Dai , Hao Lu , Guanghao Jin","doi":"10.1016/j.jksuci.2024.102194","DOIUrl":"10.1016/j.jksuci.2024.102194","url":null,"abstract":"<div><div>In this era of Transformers enjoying remarkable success, Convolutional Neural Networks (CNNs) remain highly relevant and useful. Indeed, hybrid Transformer-CNN network architectures, which combine the benefits of both approaches, have achieved impressive results. Vision Transformer (ViT) is a significant neural network architecture that features a convolutional layer as its first layer, primarily built on the transformer framework. However, owing to the distinct computation patterns inherent in attention and convolution, existing hardware accelerators for these two models are typically designed separately and lack a unified approach toward accelerating both models efficiently. In this paper, we present a dedicated accelerator on a field-programmable gate array (FPGA) platform. The accelerator, which integrates a configurable three-dimensional systolic array, is specifically designed to accelerate the inferential capabilities of hybrid Transformer-CNN networks. The Convolution and Transformer computations can be mapped to a systolic array by unifying these operations for matrix multiplication. Softmax and LayerNorm which are frequently used in hybrid Transformer-CNN networks were also implemented on FPGA boards. The accelerator achieved high performance with a peak throughput of 722 GOP/s at an average energy efficiency of 53 GOPS/W. Its respective computation latencies were 51.3 ms, 18.1 ms, and 6.8 ms for ViT-Base, ViT-Small, and ViT-Tiny. The accelerator provided a <span><math><mrow><mn>12</mn><mo>×</mo></mrow></math></span> improvement in energy efficiency compared to the CPU, a <span><math><mrow><mn>2</mn><mo>.</mo><mn>3</mn><mo>×</mo></mrow></math></span> improvement compared to the GPU, and a <span><math><mrow><mn>1</mn><mo>.</mo><mn>5</mn><mo>×</mo></mrow></math></span> to <span><math><mrow><mn>2</mn><mo>×</mo></mrow></math></span> improvement compared to existing accelerators regarding speed and energy efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A scalable attention network for lightweight image super-resolution 用于轻量级图像超分辨率的可扩展注意力网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102185

Jinsheng Fang , Xinyu Chen , Jianglong Zhao , Kun Zeng

{"title":"A scalable attention network for lightweight image super-resolution","authors":"Jinsheng Fang , Xinyu Chen , Jianglong Zhao , Kun Zeng","doi":"10.1016/j.jksuci.2024.102185","DOIUrl":"10.1016/j.jksuci.2024.102185","url":null,"abstract":"<div><div>Modeling long-range dependencies among features has become a consensus to improve the results of single image super-resolution (SISR), which stimulates interest in enlarging the kernel sizes in convolutional neural networks (CNNs). Although larger kernels definitely improve the network performance, network parameters and computational complexities are raised sharply as well. Hence, an optimization of setting the kernel sizes is required to improve the efficiency of the network. In this work, we study the influence of the positions of larger kernels on the network performance, and propose a scalable attention network (SCAN). In SCAN, we propose a depth-related attention block (DRAB) that consists of several multi-scale information enhancement blocks (MIEBs) and resizable-kernel attention blocks (RKABs). The RKAB dynamically adjusts the kernel size concerning the locations of the DRABs in the network. The resizable mechanism allows the network to extract more informative features in shallower layers with larger kernels and focus on useful information in deeper layers with smaller ones, which effectively improves the SR results. Extensive experiments demonstrate that the proposed SCAN outperforms other state-of-the-art lightweight SR methods. Our codes are available at <span><span>https://github.com/ginsengf/SCAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0