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A fast parallel algorithm for blind estimation of noise variance

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3 Author(s)
P. Meer ; Center for Autom. Res., Maryland Univ., College Park, MD, USA ; J. -M. Jolion ; A. Rosenfeld

A blind noise variance algorithm that recovers the variance of noise in two steps is proposed. The sample variances are computed for square cells tessellating the noise image. Several tessellations are applied with the size of the cells increasing fourfold for consecutive tessellations. The four smallest sample variance values are retained for each tessellation and combined through an outlier analysis into one estimate. The different tessellations thus yield a variance estimate sequence. The value of the noise variance is determined from this variance estimate sequence. The blind noise variance algorithm is applied to 500 noisy 256×256 images. In 98% of the cases, the relative estimation error was less than 0.2 with an average error of 0.06. Application of the algorithm to differently sized images is also discussed

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:12 ,  Issue: 2 )