  Deep image prior (DIP) is a recently proposed technique for solving imaging
inverse problems by fitting the reconstructed images to the output of an
untrained convolutional neural network. Unlike pretrained feedforward neural
networks, the same DIP can generalize to arbitrary inverse problems, from
denoising to phase retrieval, while offering competitive performance at each
task. The central disadvantage of DIP is that, while feedforward neural
networks can reconstruct an image in a single pass, DIP must gradually update
its weights over hundreds to thousands of iterations, at a significant
computational cost. In this work we use meta-learning to massively accelerate
DIP-based reconstructions. By learning a proper initialization for the DIP
weights, we demonstrate a 10x improvement in runtimes across a range of inverse
imaging tasks. Moreover, we demonstrate that a network trained to quickly
reconstruct faces also generalizes to reconstructing natural image patches.
