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Abstract #1774

ENSURE: Ensemble Stein’s Unbiased Risk Estimator for Unsupervised Learning

Hemant Kumar Aggarwal1, Aniket Pramanik1, and Mathews Jacob1
1Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States

Deep learning algorithms are emerging as powerful alternatives to compressed sensing methods, offering improved image quality and computational efficiency. Fully sampled training images are often difficult to acquire in high-resolution and dynamic imaging applications. We propose an ENsemble SURE (ENSURE) loss metric to train a deep network only from undersampled measurements. In particular, we show that training a network using an ensemble of images, each acquired with a different sampling pattern, using ENSURE can provide results that closely approach MSE training. Our experimental results show comparable reconstruction quality to supervised learning.

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