  We present a neural architecture search (NAS) technique to enhance the
performance of unsupervised image de-noising, in-painting and super-resolution
under the recently proposed Deep Image Prior (DIP). We show that evolutionary
search can automatically optimize the encoder-decoder (E-D) structure and
meta-parameters of the DIP network, which serves as a content-specific prior to
regularize these single image restoration tasks. Our binary representation
encodes the design space for an asymmetric E-D network that typically converges
to yield a content-specific DIP within 10-20 generations using a population
size of 500. The optimized architectures consistently improve upon the visual
quality of classical DIP for a diverse range of photographic and artistic
content.
