  A problem of image denoising when images are corrupted by a non-stationary
noise is considered in this paper. Since in practice no a priori information on
noise is available, noise statistics should be pre-estimated for image
denoising. In this paper, deep convolutional neural network (CNN) based method
for estimation of a map of local, patch-wise, standard deviations of noise
(so-called sigma-map) is proposed. It achieves the state-of-the-art performance
in accuracy of estimation of sigma-map for the case of non-stationary noise, as
well as estimation of noise variance for the case of additive white Gaussian
noise. Extensive experiments on image denoising using estimated sigma-maps
demonstrate that our method outperforms recent CNN-based blind image denoising
methods by up to 6 dB in PSNR, as well as other state-of-the-art methods based
on sigma-map estimation by up to 0.5 dB, providing same time better usage
flexibility. Comparison with the ideal case, when denoising is applied using
ground-truth sigma-map, shows that a difference of corresponding PSNR values
for most of noise levels is within 0.1-0.2 dB and does not exceeds 0.6 dB.
