Abstract: Considering the fact that the smallest eigenvalue of covariance matrix of the raw patches extracted from noise images is significantly correlated with noise level,this paper proposed a fast algorithm that directly uses a pretrained nonlinear mapping model based on the polynomial regression to map (rectify) the smallest eigenvalue to the final estimate.Firstly,some representative natural images without distortion are selected as training set.Then,the training sample library is formed,and the training set images are corrupted with the different noise levels.Based on this,raw patches are extracted for each noisy image,and the smallest eigenvalue of covariance matrix of the raw patches is gotten by PCA transformation.Finally,a nonlinear mapping model between the smallest eigenvalue and the noise level are obtained based on polynomial regression technique.Extensive experiments show that the proposed algorithm works well for a wide range of noise levels and has outstanding performance at low levels in particular compared with the existing algorithms,showing a good compromise between speed and accuracy in general.

Key words: Image denoising, Low level noise, Noise level estimation, Principal component analysis, Rectification function, Smallest eigenvalue