DocumentCode :
2797219
Title :
Fundamental limits of image denoising: Are we there yet?
Author :
Chatterjee, Priyam ; Milanfar, Peyman
Author_Institution :
Dept. of Electr. Eng., Univ. of California, Santa Cruz, CA, USA
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
1358
Lastpage :
1361
Abstract :
In this paper, we study the fundamental performance limits of image denoising where the aim is to recover the original image from its noisy observation. Our study is based on a general class of estimators whose bias can be modeled to be affine. A bound on the performance in terms of mean squared error (MSE) of the recovered image is derived in a Bayesian framework. In this work, we assume that the original image is available, from which we learn the image statistics. Performances of some current state-of-the-art methods are compared to our MSE bounds for some commonly used experimental images. These show that some gain in denoising performance is yet to be achieved.
Keywords :
Bayes methods; image denoising; mean square error methods; Bayesian framework; MSE bounds; image denoising; image recovery; image statistics; mean squared error; Bayesian methods; Degradation; Gaussian noise; Hardware; Image denoising; Noise reduction; Performance gain; Solid modeling; Statistics; Yield estimation; Bayesian Cramér-Rao lower bound; Image denoising; estimation; mean squared error;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495447
Filename :
5495447
Link To Document :
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