Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67513
Title: Patch group based nonlocal self-similarity prior learning for image denoising
Authors: Xu, J
Zhang, L 
Zuo, W
Zhang, D 
Feng, X
Keywords: Noise reduction
Image denoising
Image restoration
Training
Noise measurement
Dictionaries
Wavelet transforms
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 Dec 2015, p.244-252 How to cite?
Abstract: Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.
URI: http://hdl.handle.net/10397/67513
ISBN: 978-1-4673-8391-2 (electronic)
978-1-4673-8390-5 (USB)
EISSN: 2380-7504
DOI: 10.1109/ICCV.2015.36
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

18
Citations as of Sep 16, 2017

WEB OF SCIENCETM
Citations

14
Citations as of Sep 16, 2017

Page view(s)

3
Last Week
1
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.