Suchergebnisse - multi-layered convolutional sparse coding

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  1. 1

    Analysis of Top-Down Connections in Multi-Layered Convolutional Sparse Coding von Edlund, Joakim, Guillemot, Christine, Sjostrom, Marten

    ISSN: 2473-3628
    Veröffentlicht: IEEE 06.10.2021
    “… Multi-Layered Convolutional Sparse Coding (ML-CSC) based on a cascade of convolutional layers in which each layer can be approximately explained by the following layer can be seen as a biologically inspired framework …”
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  2. 2

    Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction von Wahid, Abdul, Shah, Jawad Ali, Khan, Adnan Umar, Ahmed, Manzoor, Razali, Hanif

    ISSN: 2169-3536, 2169-3536
    Veröffentlicht: Piscataway IEEE 2020
    Veröffentlicht in IEEE access (2020)
    “… Inspired from recent research works on multi-layer convolutional sparse coding (ML-CSC) theory to model deep neural networks, this work proposes a multi-layer basis …”
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    Journal Article
  3. 3

    Multi-Layered Basis Pursuit Algorithms for Classification of MR Images of Knee ACL Tear von Wahid, Abdul, Shah, Jawad Ali, Khan, Adnan Umar, Ullah, Mukhtar, Ayob, Mohd Zaki

    ISSN: 2169-3536, 2169-3536
    Veröffentlicht: Piscataway IEEE 2020
    Veröffentlicht in IEEE access (2020)
    “… Deep learning architectures have been extensively used in recent years for the classification of biomedical images to assist clinicians for diagnosis and …”
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    Journal Article
  4. 4

    Multi Attention Convolutional Sparse Coding U-Net for Enhanced Land-Use and Land-Cover Segmentation Using Hyperspectral Images von Yele, Vijaykumar P., Badhe, Namdeo Baban, Alegavi, Sujata, Sedamkar, R. R.

    ISSN: 1557-2072, 1557-2064, 1557-2072
    Veröffentlicht: New York Springer US 01.12.2025
    Veröffentlicht in Sensing and imaging (01.12.2025)
    “… with the architectural complexities hindering the performance across different datasets and tasks. In response to this, a Multi Attention Convolutional sparse coding U-network Feed-forward Network (MACUFN …”
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    Journal Article
  5. 5

    Lamb wave-based damage assessment for composite laminates using a deep learning approach von Zhang, Han, Wang, Fan, Lin, Jing, Hua, Jiadong

    ISSN: 0041-624X, 1874-9968, 1874-9968
    Veröffentlicht: Netherlands Elsevier B.V 01.07.2024
    Veröffentlicht in Ultrasonics (01.07.2024)
    “… •A convolutional sparse coding-based UNet (CSCUNet) is proposed to analyze the ultrasonic Lamb wave signal, which enables high-resolution imaging of damage in composite laminates …”
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    Journal Article
  6. 6

    A Multilayered Convolutional Sparse Coding Framework for Modeling of Pooling Operation of Convolution Neural Networks von Wahid, Abdul, Umar Khan, Adnan, Mukhtarullah, Khan, Sheroz, Shah, Jawad

    ISSN: 2640-6535
    Veröffentlicht: IEEE 01.08.2019
    “… Convolutional Sparse Coding (CSC) framework has been proposed recently to explain relation between Convolutional Neural Networks (CNNs …”
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  7. 7

    Improved Image Generation via Sparse Modeling von Ganz, Roy, Elad, Michael

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 13.05.2022
    Veröffentlicht in arXiv.org (13.05.2022)
    “… The interest of the deep learning community in image synthesis has grown massively in recent years. Nowadays, deep generative methods, and especially …”
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  8. 8

    Complex Valued and Layer Based Categorization of Synthetic Aperture Radar Patches von Sipos, Danijel, Gleich, Dusan, Planinsic, Peter

    Veröffentlicht: IEEE 20.10.2021
    “… In this paper a sparse coding approach is designed in multi layered architecture. This this paper proposes a layered based sparse coding using approaches introduced within …”
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  9. 9

    Multi-layer Convolutional Sparse Coding Framework for Restoration of Under-sampled MR Images von Wahid, Abdul, Ullah, Abdul Wahab Usman, Kadir, Kushairy, Saadain, Syed

    ISSN: 2642-2077
    Veröffentlicht: IEEE 17.05.2021
    “… Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis of different pathologies associated with human anatomy. The need to acquire images …”
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