NDLI: Image denoising using hyper-Laplacian priors and gradient histogram preservation model

Content Provider	IEEE Xplore Digital Library
Author	Jia, Fuqing Zhang, Hongzhi Deng, Hong Li, Wei Zuo, Wangmeng
Copyright Year	2014
Description	Author affiliation: School of Computer Science and Technology, Harbin Institute of Technology, China (Jia, Fuqing; Zhang, Hongzhi; Deng, Hong; Zuo, Wangmeng) \|\| Shenzhen Graduate School, Harbin Institute of Technology, China (Li, Wei)
Abstract	Image noise is difficult to avoid during the image acquisition and communication, and thus we need to suppress noise in the low level vision. Among all of the existing image denoising methods, image priors, such as hyper-Laplacian priors of the heavy-tailed distribution of image gradient, play an important role. However, many denoising methods tend to smooth the fine textures while suppressing noise, degrading the image visual quality. In order to solve this problem, we introduce a gradient histogram preservation (GHP) model. Combining the GHP model with the hyper-Laplacian priors, we can improve the denoising performance. We use the alternating minimization scheme, among the two phases is a non-convex L - minimization problem. The problem can be solved by two methods, the lookup table (LUT) and the generalized iterative shrinkage algorithm (GISA). Furthermore, the experimental results demonstrate that with the gradient histogram preservation model, we can preserve the image textures while removing noise, leading to a much more natural denoising result.
Starting Page	811
Ending Page	815
File Size	1927574
Page Count	5
File Format	PDF
ISBN	9781479921881
ISSN	21645221
e-ISBN	9781479921867
DOI	10.1109/ICOSP.2014.7015116
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image denoising Histograms Noise reduction Noise Minimization Laplace equations Image restoration alternating minimization image denoising gradient histogram perservation hyper-Laplacian
Content Type	Text
Resource Type	Article

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