On required accuracy of mixed noise parameter estimation for image enhancement via denoising

Characteristics of noise (type, statistics, spatial correlation) are nowadays exploited in many image denoising and enhancement methods. However, these characteristics are often unknown, and they have to be extracted from an image at hand. There are many powerful and accurate blind methods for noise...

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Bibliographic Details
Published in:EURASIP journal on image and video processing 2014-01, Vol.2014 (1), p.1-16, Article 3
Main Authors: Abramova, Victoriya V, Abramov, Sergey K, Lukin, Vladimir V, Egiazarian, Karen O, Astola, Jaakko T
Format: Article
Language:English
Subjects:
Biometrics
Engineering
Filtering
Filtration
Image enhancement
Image Processing and Computer Vision
Mathematical models
Noise
Noise reduction
Parameter estimation
Pattern Recognition
Signal,Image and Speech Processing
Video Quality Metrics for Consumer Electronics
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Summary:Characteristics of noise (type, statistics, spatial correlation) are nowadays exploited in many image denoising and enhancement methods. However, these characteristics are often unknown, and they have to be extracted from an image at hand. There are many powerful and accurate blind methods for noise variance estimation for the cases of additive and multiplicative noise models. However, more complicated noise models containing a mixture of signal-independent (SI) and signal-dependent (SD) components are often more adequate in practice. Parameters of both components have to be automatically estimated to be used in image enhancement. This paper addresses a question of required accuracy of such estimation. Analysis is carried out for color images processed by a filter based on discrete cosine transform. The influence of errors in mixed noise parameters estimation is studied in terms of filtering efficiency. This efficiency is characterized by the conventional criterion peak signal-to-noise ratio (PSNR) and two visual quality metrics, PSNR human visual system masking (PSNR-HVS-M) and multi-scale structural similarity (MSSIM). If a reduction of filtering efficiency exceeds 0.5 dB (in terms of PSNR and PSNR-HVS-M) or 0.005 (in terms of MSSIM), mixed noise parameters estimation is assumed to be unacceptable. As the result, it is shown that SI and SD noise parameters have to be estimated with a relative error not exceeding 20%…30%.
ISSN:1687-5281
1687-5176
1687-5281
DOI:10.1186/1687-5281-2014-3