Abstract
The problem of automatic detection of image areas appropriate for accurate estimation of additive noise standard deviation (STD) irrespectively to processed image properties is considered in this paper. For accurate estimation of either image texture or noise STD, we distinguish two complementary informative maps: noise- (NI-) and texture- (TI-) informative ones. The NI map is determined and iteratively upgraded based on the Fisher information on noise STD calculated in scanning window (SW) fashion. Fractional Brownian motion (fBm) model for image texture is used to derive the required Fisher information. To extract final noise STD from NI map, fBm- and DCT-based estimators are implemented. The performance of these two estimators is comparatively assessed on large image database for different noise levels. It is also compared with performance of two competitive state-of-the-art estimators recently published. Utilizing NI map along with DCT-based noise STD estimator has proved to be significantly more efficient.
Highlights
Images formed by multispectral sensors or digital cameras are subject to undesirable errors including sensor random noise, blur, distortions, radiometrical, and geometrical errors
We propose on one hand, to predict texture parameters in NI map with parameters estimated from neighboring TI map
We introduce Fractional Brownian motion (fBm) model for image texture in an N × N scanning window (SW) centered at (t0, s0) : x(t, s) = BH (t, s) + m(t0,s0), t = t0 − Nh, . . . , t0 + Nh, s = s0 − Nh, . . . , s0 + Nh, where BH (t, s) is 2D fBm field, m(t0,s0) is a mean bias, Nh is a half size of the SW, N = 2Nh + 1
Summary
Images formed by multispectral sensors or digital cameras are subject to undesirable errors including sensor random noise, blur, distortions, radiometrical, and geometrical errors. We hope to obtain quite accurate estimates of texture and noise parameters available for any SW in the whole image Based on this knowledge, both informative maps can be in turn refined and upgraded by considering CRLB-based criterion once again. Texture amplitude can vary significantly (change in light conditions is an example), and it is possible to find both TI and NI SWs within image local neighborhood This assumption, though simple (e.g., it does not take into account image edges), allows developing maximum likelihood (ML) noise STD estimator and confirming the practical interest of using TI and NI maps as additional sources of useful information on both texture and noise local parameters.
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