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

Citation Author(s):
Christopher A. Metzler, Frank Ong, Gordon Wetzstein
Submitted by:
Ruangrawee Kiti...
Last updated:
22 June 2021 - 9:22am
Document Type:
Presentation Slides
Document Year:
Presenters Name:
Ruangrawee Kitichotkul



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.

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Dataset Files

slides for presentation at ICASSP 2021