DocumentCode :
249289
Title :
Homogeneity classification for signal-dependent noise estimation in images
Author :
Rakhshanfar, Meisam ; Amer, Aishy
Author_Institution :
Concordia Univ., Montreal, QC, Canada
fYear :
2014
fDate :
27-30 Oct. 2014
Firstpage :
4271
Lastpage :
4275
Abstract :
This paper presents a fast method to estimate the noise level in real images, and attempts to solve clipping and signal-dependency problems for robust noise estimation. We propose an intensity-variance homogeneity classification technique to classify images corrupted with additive Poisson-Gaussian noise based on intensity and variance. Benefiting from signal-independency in each intensity class, this method localizes the noise-representative homogenous regions in the image. Experimental results show the proposed method rivals state-of-the-art estimation approaches, while it is fast.
Keywords :
Gaussian noise; image classification; image denoising; additive Poisson-Gaussian noise; intensity-variance homogeneity classification technique; real images; signal-dependent noise estimation; AWGN; Estimation; Image processing; Noise level; Noise measurement; Videos; Noise estimation; homogeneity analysis; signal dependent;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
Type :
conf
DOI :
10.1109/ICIP.2014.7025867
Filename :
7025867
Link To Document :
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