Abstract
Deep image prior was recently introduced as an effective prior for image reconstruction. It 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 fits the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques. Our work develops a two-stage learning paradigm to address the computational challenge: (i) we perform a supervised pretraining of the network on a synthetic dataset; (ii) we fine-tune the network's parameters to adapt to the target reconstruction. We showcase that pretraining considerably speeds up the subsequent reconstruction from real-measured micro computed tomography data of biological specimens. The code and additional experimental materials are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2111.11926
