基于自注意力生成对抗网络的图像超分辨率重建

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...

Image super-resolution reconstruction based on self-attention GAN

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.