1,083 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
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern
denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or
other proximal algorithms. An advantage of PnP is that one can use pre-trained
denoisers when there is not sufficient data for end-to-end training. Although
PnP has been recently studied extensively with great empirical success,
theoretical analysis addressing even the most basic question of convergence has
been insufficient. In this paper, we theoretically establish convergence of
PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain
Lipschitz condition on the denoisers. We then propose real spectral
normalization, a technique for training deep learning-based denoisers to
satisfy the proposed Lipschitz condition. Finally, we present experimental
results validating the theory.Comment: Published in the International Conference on Machine Learning, 201
Dilated Deep Residual Network for Image Denoising
Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoising have many layers. In such cases, the models involve a large
amount of parameters and are computationally expensive to train. In this paper,
we develop a dilated residual CNN for Gaussian image denoising. Compared with
the recently proposed residual denoiser, our method can achieve comparable
performance with less computational cost. Specifically, we enlarge receptive
field by adopting dilated convolution in residual network, and the dilation
factor is set to a certain value. We utilize appropriate zero padding to make
the dimension of the output the same as the input. It has been proven that the
expansion of receptive field can boost the CNN performance in image
classification, and we further demonstrate that it can also lead to competitive
performance for denoising problem. Moreover, we present a formula to calculate
receptive field size when dilated convolution is incorporated. Thus, the change
of receptive field can be interpreted mathematically. To validate the efficacy
of our approach, we conduct extensive experiments for both gray and color image
denoising with specific or randomized noise levels. Both of the quantitative
measurements and the visual results of denoising are promising comparing with
state-of-the-art baselines.Comment: camera ready, 8 pages, accepted to IEEE ICTAI 201
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