A fusion denoising method based on homomorphic transform and 3D transform-domain collaborative filtering for laser speckle imaging of blood flow

Fu, Xuenian; Wu, Sijin; Si, Juanning; Li, Weixian

doi:10.1080/09500340.2022.2146224

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Li Caoyuan et al [21][22][23][24] used an image denoising technique based on nonlocal Bayesian singular value thresholding and the Stein unbiased risk estimator. Fu Xuenian et al [25][26][27][28] proposed a fusion denoising method for laser blood flow speckle imaging based on homomorphic transform and three-dimensional transform domain synergistic filtering, which is widely used, but only in medical species. Sohlberg Antt et al [29,30] apply deep learning and generative networks to process noise, but realtime performance needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method for Multi-Noise on Steel Surface Detection

Chen,

Wang,

et al. 2023

Applied Sciences

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In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.

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