| Paper ID | IVMSP-7.2 |
| Paper Title |
Gating Feature Dense Network for Single Anisotropic MR Image Super-resolution |
| Authors |
Weidong He, Chongqing University, China; Yangjinan Hu, Columbia University, China; Lulu Wang, Zhongshi He, Chongqing University, China; Jinglong Du, Chongqing Medical University, China |
| Session | IVMSP-7: Machine Learning for Image Processing I |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
High resolution (HR) magnetic resonance (MR) images are crucial for medical diagnosis. However, in practice, low resolution MR images are often acquired due to hardware limitation. In this work, we propose a gating feature dense net- work to reconstruct HR MR images from low resolution acquisitions, where we use local residual dense block (LRDB) as the backbone. We propose gating mechanism, which includes absorption gate and release gate, to adaptively introduce the informative features of previous LRDBs to current LRDB to solve the problem of insufficient features sharing. The absorption gate can fuse the output feature of LRDBs with adaptive weights, which allows the model to adaptively learn the effects of different LRDBs for MR image super- resolution (SR). Experimental results show that our proposed method achieves a new state-of-the-art quantitative and visual performance in MR image SR. |
