Application of attention-DnCNN for ESPI fringe patterns denoising.

Journal: Journal of the Optical Society of America. A, Optics, image science, and vision
PMID: 36520708

Abstract

Fringe patterns' denoising in electronic speckle pattern interferometry (ESPI) is an important step in phase extraction. In this study, we propose a new denoising method for ESPI fringe patterns based on a convolutional neural network (CNN). The proposed model which combines the attention mechanism and CNN is defined as attention-denoising CNN. In this model, owing to the attention mechanism, more attention will be paid to fringe information, and better filtering results will be achieved. The experimental results show that our proposed method can obtain excellent results, especially with high and large variation density ESPI fringe patterns.

Authors

  • Linlin Wang
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Run Li
    School of Nursing, Shanxi University of Chinese Medicine, Shanxi, Taiyuan 030024, China.
  • Feng Tian
    Bioinformatics Graduate Program, and Department of Biomedical Engineering, Boston. University, 24 Cummington Mall, Boston, MA 02215, USA.
  • Xiaoyu Fang
    School of Pharmacy and Food Engineering, Wuyi University, Jiangmen, China.

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