Paper Info
Title
Fast and reliable noise estimation algorithm based on statistical hypothesis tests
Abstract
Image noise estimation is a very important topic in digital image processing. This paper presents a fast and reliable noise estimation algorithm for additive white Gaussian noise (WGN). The proposed algorithm provides a way to measure the degree of image feature based on statistical hypothesis tests (SHT). Firstly, the proposed algorithm distinguishes homogeneous blocks and non-homogeneous blocks by the degree of image feature, and then sets the minimal variance of these homogeneous blocks as a reference variance. Secondly, the proposed algorithm finds more homogeneous blocks whose variances are similar to the reference variance and which are not non-homogeneous blocks. Lastly, the noise variance is estimated from these homogeneous blocks by a weighted averaging process according to the degree of image feature. Experiments show that the proposed algorithm performs well and reliably for different types of images over a large range of noise levels.
Year
DOI
Venue
2012
10.1109/VCIP.2012.6410754
VCIP
Keywords
Field
DocType
statistical hypothesis tests,statistical hypothesis test,noise variance,statistical testing,nonhomogeneous block,noise estimation,awgn,minimal variance,reference variance,degree of image feature,image denoising,noise level,digital image processing,noisy image,weighted averaging process,feature extraction,additive white gaussian noise,homogeneous block,image noise estimation algorithm,white gaussian noise
Value noise,Median filter,Pattern recognition,Noise measurement,Computer science,Salt-and-pepper noise,Algorithm,Image noise,White noise,Artificial intelligence,Gaussian noise,Gradient noise
Conference
ISBN
Citations 
PageRank 
978-1-4673-4406-7
2
0.39
References 
Authors
7
2
Name
Order
Citations
PageRank
Ping Jiang1172.34
Jian-Zhou Zhang2225.38