Abstract
Model-based optimization methods and discriminative
learning methods have been the two dominant strategies for
solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods
are flexible for handling different inverse problems but are
usually time-consuming with sophisticated priors for the
purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their
application range is greatly restricted by the specialized
task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in
as a modular part of model-based optimization methods to
solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the
study of integration with fast discriminative denoiser prior
is still lacking. To this end, this paper aims to train a set of
fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization
method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers can not
only achieve promising Gaussian denoising results but also
can be used as prior to deliver good performance for various low-level vision applications