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Noise level estimation based on eigenvalue learning
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AI
DOI：10.1007/s11042-023-17403-5.png)
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Abstract
En 中文
At present, many algorithms use a single minimum eigenvalue to estimate the real noise level, and the levels estimated by these algorithms have been proven to be less than the real noise levels, this is known as underestimation. To address this problem, this paper uses multiple eigenvalues to obtain the relationship between eigenvalues and the real noise level through sample training, calculates the learning coefficients for different noise levels in the relationship expression by linear fitting, and then inputs the learning coefficients into the noise image for noise level estimation. Experiments demonstrate that the algorithm proposed in this paper can significantly improve the underestimation problem of the traditional algorithm and has better estimation accuracy for various noise levels in gray images, color images, and texture images of various scenes.
Keywords:
Noise level estimation
Gaussian noise
Weak texture
Principal component analysis
Linear regression analysis
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