  Learning a good image prior is a long-term goal for image restoration and
manipulation. While existing methods like deep image prior (DIP) capture
low-level image statistics, there are still gaps toward an image prior that
captures rich image semantics including color, spatial coherence, textures, and
high-level concepts. This work presents an effective way to exploit the image
prior captured by a generative adversarial network (GAN) trained on large-scale
natural images. As shown in Fig.1, the deep generative prior (DGP) provides
compelling results to restore missing semantics, e.g., color, patch,
resolution, of various degraded images. It also enables diverse image
manipulation including random jittering, image morphing, and category transfer.
Such highly flexible restoration and manipulation are made possible through
relaxing the assumption of existing GAN-inversion methods, which tend to fix
the generator. Notably, we allow the generator to be fine-tuned on-the-fly in a
progressive manner regularized by feature distance obtained by the
discriminator in GAN. We show that these easy-to-implement and practical
changes help preserve the reconstruction to remain in the manifold of nature
image, and thus lead to more precise and faithful reconstruction for real
images. Code is available at
https://github.com/XingangPan/deep-generative-prior.
