东华大学学报（英文版）2025，Vol.42Issue(4)：435-441,7.DOI:10.19884/j.1672-5220.202403015
基于GAN和多尺度密集残差注意力机制网络的磁共振图像超分辨率重建
Magnetic Resonance Image Super-Resolution Based on GAN and Multi-Scale Residual Dense Attention Network
管纯灵 1禹素萍 1许武军 1范红1
作者信息
- 1. 东华大学信息科学与技术学院,上海 201620
- 折叠
摘要
Abstract
The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image SR may lead to issues such as blurry details and excessive smoothness.To address the limitations,we proposed an algorithm based on the generative adversarial network(GAN)framework.In the generator network,three different sizes of convolutions connected by a residual dense structure were used to extract detailed features,and an attention mechanism combined with dual channel and spatial information was applied to concentrate the computing power on crucial areas.In the discriminator network,using InstanceNorm to normalize tensors sped up the training process while retaining feature information.The experimental results demonstrate that our algorithm achieves higher peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM)compared to other methods,resulting in an improved visual quality.关键词
磁共振/图像超分辨率/注意力机制/生成对抗网络/多尺度卷积Key words
magnetic resonance(MR)/image super-resolution(SR)/attention mechanism/generative adversarial network(GAN)/multi-scale convolution分类
信息技术与安全科学引用本文复制引用
管纯灵,禹素萍,许武军,范红..基于GAN和多尺度密集残差注意力机制网络的磁共振图像超分辨率重建[J].东华大学学报（英文版）,2025,42(4):435-441,7.
