Open AccessNoise Intensity Estimation Method Based on PCA and Weak Textured Block Selection for Neutron Image
Author(s) -
Yao Pan,
Sanzheng Qiao,
Chenglin Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1739/1/012023
Subject(s) - noise reduction , noise (video) , gaussian noise , image noise , artificial intelligence , intensity (physics) , computer science , pattern recognition (psychology) , principal component analysis , image (mathematics) , mathematics , computer vision , optics , physics
Noise intensity estimation has a very important application in image denoising. In image processing, the denoising method can achieve an ideal denoising effect under the assumption that the Gaussian noise intensity in the image is known. But in real denoising applications, especially the neutron image, the noise level is unknown, which will greatly affect the denoising effect of neutron image processing. In this paper, a method which combined the principal component analysis with weak texture block selection is proposed for noise intensity estimation of neutron images. The experimental results show that the proposed method can accurately estimate the Gaussian noise in the neutron image. Compared with the existing noise intensity estimation methods, the qualitative and quantitative results show that the proposed method has higher accuracy and stability.