基于自注意力生成对抗网络的图像超分辨率重建
作者:
作者单位:

(1. 中国矿业大学地下空间智能控制教育部工程研究中心,江苏徐州221116;2. 中国矿业大学信息与控制工程学院,江苏徐州221116)

作者简介:

通讯作者:

E-mail: chengyuhu@163.com.

中图分类号:

TP273

基金项目:

国家自然科学基金项目(61772532,61976215).


Image super-resolution reconstruction based on self-attention GAN
Author:
Affiliation:

(1. Engineering Research Center of Intelligent Control for Underground Space,Ministry of Education,China University of Mining and Technology,Xuzhou221116,China;2. School of Information and Control Engineering,China University of Mining and Technology,Xuzhou221116,China)

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    摘要:

    针对如何恢复重建后超分辨率图像的纹理细节问题,提出基于自注意力生成对抗网络的图像超分辨率重建模型(SRAGAN).在SRAGAN中,基于自注意力机制和残差模块相结合的生成器用于将低分辨率图像变换为超分辨率图像,基于深度卷积网络构成的判别器试图区分重建后的超分辨率图像和真实超分辨率图像间的差异.在损失函数构造方面,一方面利用Charbonnier内容损失函数来提高图像的重建精度,另一方面使用预训练VGG网络激活前的特征值来计算感知损失以实现超分辨率图像的精确纹理细节重构.实验结果表明,SRAGAN在峰值信噪比和结构相似度分数上均优于当前流行算法,能够重构出更为真实和具有清晰纹理的图像.

    Abstract:

    Aiming at how to recover the texture details of the reconstructed super-resolution image, an image super-resolution reconstruction based on the self-attention generative adversarial network(SRAGAN) is proposed. In the SRAGAN, a generator based on a combination of the self-attention mechanism and the residual module is used to transform low-resolution into super-resolution images, while a discriminator based on the deep convolutional network tries to distinguish the difference between the reconstructed and real super-resolution images. In terms of loss function construction, on the one hand, the Charbonnier content loss function is used to improve the accuracy of image reconstruction; on the other hand, the eigenvalues before the activation layer in the pre-trained VGG network are used to calculate the perceptual loss to achieve accurate texture detail reconstruction of super-resolution images. Experiments show that the proposed SRAGAN is superior to the current popular algorithms in peak signal-to-noise ratio and structural similarity score, reconstructing more realistic images with clear textures.

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王雪松,晁杰,程玉虎.基于自注意力生成对抗网络的图像超分辨率重建[J].控制与决策,2021,36(6):1324-1332

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  • 在线发布日期: 2021-05-10
  • 出版日期: 2021-06-20