Multi-sampling and filtering technology of IRFPA

Blind source separation (BSS) based x-ray image denoising from an image sequence is proposed. Without priori knowledge, the useful image signal can be separated from an x-ray image sequence, for original images are supposed as different combinations of stable image signal and random image noise. The BSS algorithms such as fixed-point independent component analysis and second-order statistics singular value decomposition are used and compared with multi-frame averaging which is a common algorithm for improving image's signal-to-noise ratio (SNR). Denoising performance is evaluated in SNR, standard deviation, entropy, and runtime. Analysis indicates that BSS is applicable to image denoising; the denoised image's quality will get better when more frames are included in an x-ray image sequence, but it will cost more time; there should be trade-off between denoising performance and runtime, which means that the number of frames included in an image sequence is enough.

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Gaussian Noise Removal of Blind Source Separation Based on Image Sequence

Dong

Sun

2018

2018 11th International Symposium on Computational Intelligence and Design (ISCID)

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Multi-sampling and filtering technology of IRFPA

Cited by 3 publications

References 7 publications

Signal separation from X-ray image sequence using singular value decomposition

Signal separation from X-ray image sequence using singular value decomposition

Blind source separation based x-ray image denoising from an image sequence

Gaussian Noise Removal of Blind Source Separation Based on Image Sequence

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