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 IDCI-2.5
Paper Title EMPIRICALLY ACCELERATING SCALED GRADIENT PROJECTION USING DEEP NEURAL NETWORK FOR INVERSE PROBLEMS IN IMAGE PROCESSING
Authors Byung Hyun Lee, UNIST, South Korea; Se Young Chun, Seoul National University, South Korea
SessionCI-2: Computational Imaging for Inverse Problems
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Computational Imaging: [CIF] Computational Image Formation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms. One approach is to unfold finite number of iterations of conventional optimization algorithms and to learn parameters in the algorithms. However, these are forward methods and are indeed neither iterative nor convergent. Here, we present a novel DNN-based convergent iterative algorithm that accelerates conventional optimization algorithms. We train a DNN to yield parameters in scaled gradient projection method. So far, these parameters have been chosen heuristically, but have shown to be crucial for good empirical performance. In simulation results, the proposed method significantly improves the empirical convergence rate over conventional optimization methods for various large-scale inverse problems in image processing.