An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction

Understand

Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.

• DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such that the output matches the corrupted observation.
• Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques.
• To address the computational challenge, we bestow DIP with a two-stage learning paradigm: (i) perform a supervised pretraining of the network on a simulated dataset; (ii) fine-tune the network's parameters to adapt to the target reconstruction task.