2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | CI-1.5 | ||
| Paper Title | SUREmap: Predicting Uncertainty in CNN-based Image Reconstructions using Stein's Unbiased Risk Estimate | ||
| Authors | Ruangrawee Kitichotkul, Christopher Metzler, Frank Ong, Gordon Wetzstein, Stanford University, United States | ||
| Session | CI-1: Theory for Computational Imaging | ||
| Location | Gather.Town | ||
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
| Presentation | Poster | ||
| Topic | Computational Imaging: [IMT] Computational Imaging Methods and Models | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critical applications like medical imaging: Is that blob in the reconstruction an artifact or a tumor? In this work we use Stein's unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. These heatmaps tell end-users how much to trust an image formed by a CNN, which could greatly improve the utility of CNNs in various computational imaging applications. | ||