Image noise-informative map for noise standard deviation estimation

The problem of automatic detection of image areas that can be reliably selected for accurate estimation of additive noise standard deviation (STD), irrespectively to processed image properties, is considered in this paper. For getting accurate estimate of either texture or noise parameters involved, we distinguish two complementary image informative maps: (1) noise-informative (NI) map and (2) its complementary texture-informative (TI) map. The NI map is determined and iteratively upgraded based on the Fisher information on noise STD calculated in a single scanning window (SW). The TI map is simply evolved as the complementary part of N map currently updated. Final noise STD estimation is performed by efficient analysis of finite size 9×9 block DCT coefficients in NI SWs. Experiments on large image database have proved that the proposed approach outperforms state-of the-art estimators with respect to both noise STD estimates bias and variance.