  We present an algorithm to directly solve numerous image restoration problems
(e.g., image deblurring, image dehazing, image deraining, etc.). These problems
are highly ill-posed, and the common assumptions for existing methods are
usually based on heuristic image priors. In this paper, we find that these
problems can be solved by generative models with adversarial learning. However,
the basic formulation of generative adversarial networks (GANs) does not
generate realistic images, and some structures of the estimated images are
usually not preserved well. Motivated by an interesting observation that the
estimated results should be consistent with the observed inputs under the
physics models, we propose a physics model constrained learning algorithm so
that it can guide the estimation of the specific task in the conventional GAN
framework. The proposed algorithm is trained in an end-to-end fashion and can
be applied to a variety of image restoration and related low-level vision
problems. Extensive experiments demonstrate that our method performs favorably
against the state-of-the-art algorithms.
