Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP

• URL: http://arxiv.org/abs/2111.10892v1
• Date: Sun, 21 Nov 2021 20:11:56 GMT
• Title: Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP
• Authors: Maneesh John, Hemant Kumar Aggarwal, Qing Zou, Mathews Jacob
• Abstract summary: Training data is scarce in many imaging applications, including ultra-high-resolution imaging. Deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely eliminating the need for training data. We introduce a generalized Stein's unbiased risk estimate (GSURE) loss metric to minimize the overfitting.
• Score: 31.408877556706376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning algorithms that rely on extensive training data are revolutionizing image recovery from ill-posed measurements. Training data is scarce in many imaging applications, including ultra-high-resolution imaging. The deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely eliminating the need for training data. A challenge with this scheme is the need for early stopping to minimize the overfitting of the CNN parameters to the noise in the measurements. We introduce a generalized Stein's unbiased risk estimate (GSURE) loss metric to minimize the overfitting. Our experiments show that the SURE-DIP approach minimizes the overfitting issues, thus offering significantly improved performance over classical DIP schemes. We also use the SURE-DIP approach with model-based unrolling architectures, which offers improved performance over direct inversion schemes.

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