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1 February 2018 Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification
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Abstract
We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.
© 2018 SPIE and IS&T 1017-9909/2018/$25.00 © 2018 SPIE and IS&T
Shaoping Xu, Xiaoxia Zeng, Yinnan Jiang, and Yiling Tang "Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification," Journal of Electronic Imaging 27(1), 010501 (1 February 2018). https://doi.org/10.1117/1.JEI.27.1.010501
Received: 8 October 2017; Accepted: 3 January 2018; Published: 1 February 2018
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