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A Fast Parallel Algorithm for Blind Estimation of Noise Variance
February 1990 (vol. 12 no. 2)
pp. 216-223

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.

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Index Terms:
computerised picture processing; image pyramids; fast parallel algorithm; blind noise variance; noise image; tessellations; outlier analysis; variance estimate sequence; computerised picture processing; estimation theory; noise; parallel processing
Citation:
P. Meer, J.M. Jolion, A. Rosenfeld, "A Fast Parallel Algorithm for Blind Estimation of Noise Variance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 2, pp. 216-223, Feb. 1990, doi:10.1109/34.44408
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