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A Heterogeneous Group CNN for Image Super-Resolution

Tian, Chunwei ; Zhang, Yanning ; Zuo, Wangmeng ; Lin, Chia-Wen ; Zhang, David ; Yuan, Yixuan

IEEE transaction on neural networks and learning systems, 2022-10, Vol.PP, p.1-13

United States: IEEE

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  • Título:
    A Heterogeneous Group CNN for Image Super-Resolution
  • Autor: Tian, Chunwei ; Zhang, Yanning ; Zuo, Wangmeng ; Lin, Chia-Wen ; Zhang, David ; Yuan, Yixuan
  • Assuntos: Computer architecture ; Convolution ; Convolutional neural networks ; Feature extraction ; Heterogeneous group convolutional architecture ; image super-resolution (SR) ; multilevel enhancement mechanism ; Network architecture ; Superresolution ; symmetric architecture ; Training
  • É parte de: IEEE transaction on neural networks and learning systems, 2022-10, Vol.PP, p.1-13
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR) under complex scenes. In this article, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance the internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent the appearance of obtained redundant features, a refinement block (RB) with signal enhancements in a serial way is designed to filter useless information. To prevent the loss of original information, a multilevel enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel upsampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://github.com/hellloxiaotian/HGSRCNN.
  • Editor: United States: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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