Image Super-Resolution Reconstruction Method Based on Attentive Generative Adversarial Network
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    Abstract:

    The existing image super-resolution reconstruction method based on deep learning is easy to generate pseudo texture, and the rich local feature layer information in the original low-resolution image is not fully utilized. In order to improve image quality, a super-resolution reconstruction method based on attentive generative adversarial is proposed. The generator part of the method is constructed by attention recursive network, and a dense residual block structure is also introduced in the network. First, the generator extracts the local feature layer information of the image by using the self-encoding structure to improve the resolution. Then, the image is corrected by the discriminator. Finally, the image is reconstructed into a high-resolution image. In a variety of networks for peak signal-to-noise ratio super-resolution evaluation methods, the experimental results show that the designed network exhibits stable training performance, improves the visual quality of the image, and has strong robustness.

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丁明航,邓然然,邵恒.基于注意力生成对抗网络的图像超分辨率重建方法.计算机系统应用,2020,29(2):205-211

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History
  • Received:July 13,2019
  • Revised:August 20,2019
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  • Online: January 16,2020
  • Published: February 15,2020
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