To date, a large number of research works have been conducted on noise level estimation (NLE) that automatically and accurately estimates the unknown noise level for an observed noisy image. Nevertheless, the state-of-the-art NLE algorithms are still limited in efficiency, which will undermine the overall execution performance of the subsequent denoiser. By making full use of the powerful nonlinear modeling capabilities of convolutional neural networks (CNNs), a shallow CNN-based noise separator with high execution efficiency for natural images was proposed to obtain the coarse noise component (difference image) from a single observed noisy image. Based on the fact that the coarse noise component tends to follow a Gaussian-like distribution, we chose to model it with the generalized Gaussian distribution model, whose parameters are strongly sensitive to noise level and can be treated as features to characterize the degradation degree of a given noisy image. As such, the extracted features were instantly mapped to their corresponding noise level via a back-propagation (BP) neural network pre-trained on the representative training samples, resulting in a fast yet reliable NLE algorithm. Experiments demonstrate that our training-based NLE algorithm exploiting the shallow CNN-based noise separator and BP network outperforms the state-of-the-art counterparts on estimating noise level with the least executing time over a wide range of image contents and noise levels, providing a highly effective solution to blind denoising as the preprocessing module of a non-blind denoiser.

中文翻译：

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使用基于浅层 CNN 的噪声分离器和 BP 网络的快速而可靠的噪声水平估计算法

迄今为止，已经进行了大量关于噪声水平估计（NLE）的研究工作，这些工作可以自动准确地估计观察到的噪声图像的未知噪声水平。尽管如此，最先进的 NLE 算法在效率上仍然受到限制，这将破坏后续降噪器的整体执行性能。充分利用卷积神经网络（CNNs）强大的非线性建模能力，提出了一种对自然图像具有高执行效率的基于浅层CNN的噪声分离器，从单个观察到的噪声图像中获取粗噪声分量（差异图像） . 基于粗噪声分量往往遵循类高斯分布的事实，我们选择用广义高斯分布模型对其进行建模，其参数对噪声水平非常敏感，可以作为特征来表征给定噪声图像的退化程度。因此，提取的特征通过在代表性训练样本上预先训练的反向传播 (BP) 神经网络立即映射到其相应的噪声水平，从而产生快速而可靠的 NLE 算法。实验表明，我们的基于训练的 NLE 算法利用基于浅层 CNN 的噪声分离器和 BP 网络，在以最少的执行时间在广泛的图像内容和噪声级别上估计噪声级别方面优于最先进的算法，作为非盲去噪器的预处理模块，提供了一种高效的盲去噪解决方案。