UGPNet: Universal Generative Prior for Image Restoration
Hwayoon Lee Kyoungkook Kang Hyeongmin Lee Seung-Hwan Baek Sunghyun Cho
Recent image restoration methods can be broadly categorized into two classes:
(1) regression methods that recover the rough structure of the original image
without synthesizing high-frequency details and (2) generative methods that
synthesize perceptually-realistic high-frequency details even though the
resulting image deviates from the original structure of the input. While both
directions have been extensively studied in isolation, merging their benefits
with a single framework has been rarely studied. In this paper, we propose
UGPNet, a universal image restoration framework that can effectively achieve
the benefits of both approaches by simply adopting a pair of an existing
regression model and a generative model. UGPNet first restores the image
structure of a degraded input using a regression model and synthesizes a
perceptually-realistic image with a generative model on top of the regressed
output. UGPNet then combines the regressed output and the synthesized output,
resulting in a final result that faithfully reconstructs the structure of the
original image in addition to perceptually-realistic textures. Our extensive
experiments on deblurring, denoising, and super-resolution demonstrate that
UGPNet can successfully exploit both regression and generative methods for
high-fidelity image restoration.