Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein's Unbiased Risk Estimate

Ruangrawee Kitichotkul; Christopher A. Metzler; Frank Ong; Gordon Wetzstein

ICASSP 2021accepted0 citations

Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein's Unbiased Risk Estimate

Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong, Gordon Wetzstein

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.

BibTeX

@inproceedings{icassp2021_suremappredictin,
  title = {Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein's Unbiased Risk Estimate},
  author = {Ruangrawee Kitichotkul and Christopher A. Metzler and Frank Ong and Gordon Wetzstein},
  booktitle = {ICASSP 2021},
  year = {2021}
}