Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105643
Title: | Learning deep CNN denoiser prior for image restoration | Authors: | Zhang, K Zuo, W Gu, S Zhang, L |
Issue Date: | 2017 | Source: | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21-26 July 2017, Honolulu, Hawaii, p. 2808-2817 | 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. | Publisher: | Institute of Electrical and Electronics Engineers | ISBN: | 978-1-5386-0457-1 (Electronic) 978-1-5386-0458-8 (Print on Demand(PoD)) |
DOI: | 10.1109/CVPR.2017.300 | Rights: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication K. Zhang, W. Zuo, S. Gu and L. Zhang, "Learning Deep CNN Denoiser Prior for Image Restoration," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 2808-2817 is available at https://doi.org/10.1109/CVPR.2017.300. |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhang_Learning_Deep_Cnn.pdf | Pre-Published version | 1.38 MB | Adobe PDF | View/Open |
Page views
20
Citations as of May 19, 2024
Downloads
1
Citations as of May 19, 2024
SCOPUSTM
Citations
1,362
Citations as of May 17, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.