A fast parallel algorithm for blind estimation of noise variance
- 1 January 1990
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 12 (2) , 216-223
- https://doi.org/10.1109/34.44408
Abstract
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.Keywords
This publication has 4 references indexed in Scilit:
- Edge detection by associative mappingPattern Recognition, 1989
- Segmentation through variable-order surface fittingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- A survey of thresholding techniquesComputer Vision, Graphics, and Image Processing, 1988
- A Computational Approach to Edge DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1986