2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDIVMSP-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
SessionIVMSP-7: Machine Learning for Image Processing I
LocationGather.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
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.