Abstract: The existing noise level estimation (NLE) algorithms usually adopt the strategy that separates the noise signal from the content of an image to estimate its noise level. Since only a single noisy image can be exploited, these algorithms usually design a variety of complex processes to ensure the accuracy of noise separation, resulting in low execution efficiency. To this end, a novel NLE algorithm using convolutional neural network (CNN)-based noise separation model is proposed in this paper. Specifically, we first add Gaussian noise with different levels to a great amount of representative undistorted images to obtain a training database. Then, we train a CNN-based noise separation model on the training database to obtain the noise mapping from a given noisy image. Considering the fact that the coefficients of the noise mapping show Gaussian distribution behavior, we utilize the generalized Gaussian distribution (GGD) to model the coefficients of the noise mapping, and use two parameters (scale and shape) of the model as the noise level-aware features (NLAF) to describe the level of a noisy image. Finally, an improved back propagation (BP) neural network is used to map the NLAF features to the final noise level. Extensive experiments demonstrate that our method outperforms the most existing classical NLE algorithms in terms of both computational efficiency and estimation accuracy, which makes it more practical to use.