Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time
  Training
Edward P. Chandler Shirin Shoushtari Jiaming Liu M. Salman Asif Ulugbek S. Kamilov
Plug-and-Play Priors (PnP) is a well-known class of methods for solving
inverse problems in computational imaging. PnP methods combine physical forward
models with learned prior models specified as image denoisers. A common issue
with the learned models is that of a performance drop when there is a
distribution shift between the training and testing data. Test-time training
(TTT) was recently proposed as a general strategy for improving the performance
of learned models when training and testing data come from different
distributions. In this paper, we propose PnP-TTT as a new method for overcoming
distribution shifts in PnP. PnP-TTT uses deep equilibrium learning (DEQ) for
optimizing a self-supervised loss at the fixed points of PnP iterations.
PnP-TTT can be directly applied on a single test sample to improve the
generalization of PnP. We show through simulations that given a sufficient
number of measurements, PnP-TTT enables the use of image priors trained on
natural images for image reconstruction in magnetic resonance imaging (MRI).