6,587 research outputs found
Learning Deep CNN Denoiser Prior for Image Restoration
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 not only achieve promising
Gaussian denoising results but also can be used as prior to deliver good
performance for various low-level vision applications.Comment: Accepted to CVPR 2017. Code: https://github.com/cszn/ircn
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs),
CNNs are successfully used for image denoising. However, they suffer from the
following drawbacks: (i) deep network architecture is very difficult to train.
(ii) Deeper networks face the challenge of performance saturation. In this
study, the authors propose a novel method called enhanced convolutional neural
denoising network (ECNDNet). Specifically, they use residual learning and batch
normalisation techniques to address the problem of training difficulties and
accelerate the convergence of the network. In addition, dilated convolutions
are used in the proposed network to enlarge the context information and reduce
the computational cost. Extensive experiments demonstrate that the ECNDNet
outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201
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