# Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain

arXiv: https://arxiv.org/abs/2011.04145 · HTML: https://mlsanity.com/paper/2011.04145/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 24 of 24)

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- [SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image](https://mlsanity.com/paper/2305.12646/) (2023)
- [Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI](https://mlsanity.com/paper/2306.09629/) (2023)
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- [CycleGAN Models for MRI Image Translation](https://mlsanity.com/paper/2401.00023/) (2023)

## Abstract (arXiv)

Magnetic resonance imaging plays an important role in computer-aided diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, fine perceptive generative adversarial networks (FP-GANs) is proposed to produce HR MR images from low-resolution counterparts. It can cope with the detail insensitive problem of the existing super-resolution model in a divide-and-conquer manner. Specifically, FP-GANs firstly divides an MR image into low-frequency global approximation and high-frequency anatomical texture in wavelet domain. Then each sub-band generative adversarial network (sub-band GAN) conquers the super-resolution procedure of each single sub-band image. Meanwhile, sub-band attention is deployed to tune focus between global and texture information. It can focus on sub-band images instead of feature maps to further enhance the anatomical reconstruction ability of FP-GANs. In addition, inverse discrete wavelet transformation (IDWT) is integrated into model for taking the reconstruction of whole image into account. Experiments on MultiRes_7T dataset demonstrate that FP-GANs outperforms the competing methods quantitatively and qualitatively.