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dc.creatorZhang, K
dc.creatorZuo, W
dc.creatorGu, S
dc.creatorZhang, L
dc.date.accessioned2021-01-22T14:53:02Z
dc.date.available2021-01-22T14:53:02Z
dc.date.issued2017-11-06
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4839
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4857
dc.description.abstract©2017 IEEE. 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.
dc.format.extent2808-2817
dc.relation.haspartProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
dc.relation.isreferencedbyIEEE
dc.rightsAll Rights Reserved
dc.subjectcs.CV
dc.subjectcs.CV
dc.titleLearning deep CNN denoiser prior for image restoration
dc.typeArticle
dc.type.genrePre-print
dc.relation.doi10.1109/CVPR.2017.300
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-22T14:52:59Z
refterms.dateFOA2021-01-22T14:53:03Z


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