Paper Info
Title
An Optimized Pixel-Wise Weighting Approach for Patch-Based Image Denoising
Abstract
Most existing patch-based image denoising algorithms filter overlapping image patches and aggregate multiple estimates for the same pixel via weighting. Current weighting approaches always assume the restored estimates as independent random variables, which is inconsistent with the reality. In this letter, we analyze the correlation among the estimates and propose a bias-variance model to estimate the Mean Squared Error (MSE) under various weights. The new model exploits the overlapping information of the patches; it then utilizes the optimization to try to minimize the estimated MSE. Under this model, we propose a new weighting approach based on Quadratic Programming (QP), which can be embedded into various denoising algorithms. Experimental results show that the Peak Signal to Noise Ratio (PSNR) of algorithms like K-SVD and EPLL can be improved by around 0.1 dB under a range of noise levels. This improvement is promising, since it is gained independent to which image model is used, especially when the gain from designing new image models becomes less and less.
Year
DOI
Venue
2015
10.1109/LSP.2014.2350032
IEEE Signal Process. Lett.
Keywords
Field
DocType
K-SVD,quadratic programming,EPLL,overlapping image patch,optimized pixel-wise weighting approach,mean squared error estimation,image denoising,patch-based image denoising algorithm filter,peak signal to noise ratio,epll,k-svd,filtering theory,current weighting approach,mean square error methods
Noise reduction,Peak signal-to-noise ratio,Weighting,Pattern recognition,K-SVD,Non-local means,Mean squared error,Pixel,Artificial intelligence,Quadratic programming,Mathematics
Journal
Volume
Issue
ISSN
22
1
1070-9908
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
JianZhou Feng1233.61
Li Song264.39
Xiaoming Huo315724.83
Xiaokang Yang43581238.09
Wenjun Zhang51789177.28