Computer ScienceMedicineEngineering

FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution

Mingfeng Jiang, Minghao Zhi, Liying Wei, Xiaocheng Yang, Jucheng Zhang, Yongming Li, Pin Wang, Jiahao Huang, Guang Yang

2021.8.9COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

DOI: 10.1016/j.compmedimag.2021.101969

tlooto Summary

The experimental results show that the PSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed FA-GAN method are higher than the state-of-the-art reconstruction methods.

Abstract

Highlights • A fused attentive generative adversarial networks framework is proposed for MR image super-resolution.• A combination of channel attention and self-attention is used to calculate the weight parameters of the input features.• Spectral normalization process is introduced to make the discriminator network stabler.• The proposed FA-GAN method is superior to the state-of-the-art reconstruction methods.

Citation format

JIANG, Mingfeng, et al. FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution [preprint]. arXiv, 2021. arXiv:2108.03920.