Considering the fact that there exists the strong correlation between the first several eigenvalues (in ascending order)of the covariance matrix of the raw patches extracted from a noisy image and its noise level

we proposed a novel fast multiple image-based noise level estimation (FMNLE)algorithm using the principal component analysis (PCA)and the deep neural network (DNN).Specifically

we selected the first several eigenvalues of the raw patches to form a feature vector characterizing the noise level of an image.Then

we employed deep neural network to train an estimation model on a large number of representative natural images corrupted with known noise levels

by which the feature vector can be directly mapped into the corresponding noise level.To obtain higher estimation accuracy

a two-step estimation strategy was adopted.Extensive experiments show that

the estimation accuracy of the proposed algorithm is stable at each noise level with good efficiency

demonstrating a better comprehensive advantage as the pre-processing module for denoising algorithms.