A Principled Self-Referenced Early Stopping Approach for Deep Image Prior
Recently, Deep Image Prior (DIP) has demonstrated strong capabilities for solving inverse imaging problems (IIPs) by optimizing a randomly initialized convolutional neural network in a training-data-free regime. However, DIP suffers from overfitting to noisy measurements due to network over-parameterization, making early stopping (ES) essential. The most successful ES method tracks fluctuations in the running variance of the network output to detect overfitting. However, in many applications, these fluctuations may appear prematurely, leading to unstable reconstructions. In this paper, we first show that nearly optimal DIP early stopping can be achieved when two independent noisy copies o...
| Authors | Chaoyan Huang, Cheng-Han Huang, Ismail R. Alkhouri et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 May 2026 |
| Last updated at source | 27 May 2026 |
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