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  • Open Access

    ARTICLE

    Improving VQA via Dual-Level Feature Embedding Network

    Yaru Song*, Huahu Xu, Dikai Fang

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 397-416, 2024, DOI:10.32604/iasc.2023.040521

    Abstract Visual Question Answering (VQA) has sparked widespread interest as a crucial task in integrating vision and language. VQA primarily uses attention mechanisms to effectively answer questions to associate relevant visual regions with input questions. The detection-based features extracted by the object detection network aim to acquire the visual attention distribution on a predetermined detection frame and provide object-level insights to answer questions about foreground objects more effectively. However, it cannot answer the question about the background forms without detection boxes due to the lack of fine-grained details, which is the advantage of grid-based features. In… More >

  • Open Access

    ARTICLE

    Enhancing Multi-Modality Medical Imaging: A Novel Approach with Laplacian Filter + Discrete Fourier Transform Pre-Processing and Stationary Wavelet Transform Fusion

    Mian Muhammad Danyal1,2, Sarwar Shah Khan3,4,*, Rahim Shah Khan5, Saifullah Jan2, Naeem ur Rahman6

    Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340

    Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

  • Open Access

    ARTICLE

    AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

    Lirong Yin1, Lei Wang1, Siyu Lu2,*, Ruiyang Wang2, Haitao Ren2, Ahmed AlSanad3, Salman A. AlQahtani3, Zhengtong Yin4, Xiaolu Li5, Wenfeng Zheng3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2315-2347, 2024, DOI:10.32604/cmes.2024.050853

    Abstract At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel More >

  • Open Access

    ARTICLE

    Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

    Hongchi Liu1, Xing Deng1,*, Haijian Shao1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737

    Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

    Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

  • Open Access

    ARTICLE

    Modeling of Leachate Propagation in a Municipal Solid Waste Landfill Foundation

    Nadezhda Zubova*, Andrey Ivantsov

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1407-1424, 2024, DOI:10.32604/fdmp.2024.051130

    Abstract The study deals with the numerical modeling of leachate distribution in the porous medium located under a municipal solid waste disposal landfill (MSWLF). The considered three-layer system is based on geological data obtained from field measurements. For simplicity, the problem is investigated by assuming a two-component approach. Nevertheless, the heat produced by landfills due to biological and chemical processes and the thermal diffusion mechanism contributing to pollution transport are taken into account. The numerical modeling of the propagation of leachate in the considered layered porous medium is implemented for parameters corresponding to natural soil and More >

  • Open Access

    ARTICLE

    Research on Multi-Scale Feature Fusion Network Algorithm Based on Brain Tumor Medical Image Classification

    Yuting Zhou1, Xuemei Yang1, Junping Yin2,3,4,*, Shiqi Liu1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5313-5333, 2024, DOI:10.32604/cmc.2024.052060

    Abstract Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature… More >

  • Open Access

    ARTICLE

    Joint Rain Streaks & Haze Removal Network for Object Detection

    Ragini Thatikonda1, Prakash Kodali1,*, Ramalingaswamy Cheruku2, Eswaramoorthy K.V3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4683-4702, 2024, DOI:10.32604/cmc.2024.051844

    Abstract In the realm of low-level vision tasks, such as image deraining and dehazing, restoring images distorted by adverse weather conditions remains a significant challenge. The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks (CNNs), supplanting traditional methods reliant on prior knowledge. However, the evolution of CNN architectures has tended towards increasing complexity, utilizing intricate structures to enhance performance, often at the expense of computational efficiency. In response, we propose the Selective Kernel Dense Residual M-shaped Network (SKDRMNet), a flexible solution adept at balancing computational efficiency with network accuracy. A… More >

  • Open Access

    ARTICLE

    BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

    Xuejie Wang1, Jianxun Zhang1,*, Ye Tao2, Xiaoli Yuan1, Yifan Guo1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4621-4639, 2024, DOI:10.32604/cmc.2024.051556

    Abstract While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity… More >

  • Open Access

    ARTICLE

    Research on Sarcasm Detection Technology Based on Image-Text Fusion

    Xiaofang Jin1, Yuying Yang1,*, Yinan Wu1, Ying Xu2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5225-5242, 2024, DOI:10.32604/cmc.2024.050384

    Abstract The emergence of new media in various fields has continuously strengthened the social aspect of social media. Netizens tend to express emotions in social interactions, and many people even use satire, metaphors, and other techniques to express some negative emotions, it is necessary to detect sarcasm in social comment data. For sarcasm, the more reference data modalities used, the better the experimental effect. This paper conducts research on sarcasm detection technology based on image-text fusion data. To effectively utilize the features of each modality, a feature reconstruction output algorithm is proposed. This algorithm is based… More >

  • Open Access

    ARTICLE

    Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

    Chunming Wu1, Shupeng Zheng2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665

    Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

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