Author(s)

Weitao Gu ¹, Xubao Wang ¹

Affiliation(s)

¹ Beijing University of Technology, Beijing, 100124, China

Corresponding Author

Weitao Gu

ABSTRACT

The phase fringe image obtained by speckle interferometry often introduces noise due to the influence of illumination conditions, camera equipment, and the working environment. The existence of noise makes the subsequent image analysis and processing complex. With the development of computing power, theartificial neural network is a new denoising method rising in recent years. However, when usinga convolutional neural network for processing, it is challenging to obtain outstanding results due to the lack of sample numbers. Referring to the working principle of a convolutional neural network, this paper improves the denoising convolutional neural network. A learning rate calculation module based on noise estimation is added to the front end of the neural networkso that the neural network can better learn the noise characteristics in the learning process. Good results are obtained when applied to the phase fringe pattern.

KEYWORDS

Speckle interferometry, Noise estimation, Convolutional neural network

CITE THIS PAPER

Weitao Gu, Xubao Wang, Image Denoising Based on Noise Estimation for Speckle Interference Phase Fringe Image With Neural Convolution Network. Journal of Image Processing Theory and Applications (2022) Vol. 5: 47-51. DOI: http://dx.doi.org/10.23977/jipta.2022.050108.

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