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.
翻译:本文审议了在图像被非静止噪音破坏时图像失色的问题。 由于实际上没有关于噪音的先验信息, 噪音统计应该预先估计图像失色。 在本文中, 深度进化神经网络( CNN) 基于深度进化神经网络( CNN) 的估算本地、 补差、 标准噪声偏差( 所谓的 sigma- map) 地图( 所谓的 sigma- map ) 的计算方法, 达到对非静止噪音的光谱估计准确性的最新性能, 以及添加白高山噪音的情况的噪音差异估计。 使用估计的 sigma- map 图像的大规模测量实验表明, 我们的方法比基于CNNNC的失色图像最新失色方法( PSNR) 最多6 dB, 以及基于Sigma-map 估计的其他州级方法( 最高为 0.5 db) 的精确性能, 提供同样的使用灵活性。 与理想的情况相比, 当使用最差的PB 0. 0. 2 度值时, 和 0. 1 B 度值 度值 的比 0.1 度值超过0. 1 度值时, 0. 2 度值。