Rethinking Deep Image Prior for Denoising

Understand

Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems.

• Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping.
• To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress and propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise.
• We also propose the `stochastic temporal ensemble (STE)' method for incorporating techniques to further improve DIP's performance for denoising.