Translator Disclaimer
Presentation + Paper
30 March 2020 Real-time imaging through moving scattering layers via a two-step deep learning strategy
Author Affiliations +
Many methods have been demonstrated that it is possible to reconstruct an object hidden scattering layers. However, it is still a big challenge when suffer from dynamic and/or time-variant scattering media. Speckle correlation is a breakthrough technique which can noninvasively retrieve the image of object from a single-shot captured pattern but it does not allow for imaging in real time as the complicated iteration process. Recently, deep learning has attracted great attention in scattering imaging but they usually employ end-to-end mode so that the scattering medium must be fixed during the training and testing process. Here, we develop a two-step deep learning strategy for imaging through moving scattering layers. In our proposed scheme, speckle autocorrelation de-noising and object image reconstruction from autocorrelation are trained respectively by using two convolution neural network. Optical experiments show that our proposed scheme has outstanding performance for real-time imaging through moving scattering layers.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Meihua Liao, Shanshan Zheng, Dajiang Lu, Guohai Situ, and Xiang Peng "Real-time imaging through moving scattering layers via a two-step deep learning strategy", Proc. SPIE 11351, Unconventional Optical Imaging II, 113510V (30 March 2020);


Image change detection via ensemble learning
Proceedings of SPIE (May 18 2013)
Image addition and subtraction using Talbot effect
Proceedings of SPIE (October 30 1992)
High order correlations in speckle fields
Proceedings of SPIE (June 14 2006)
K-factor shadow removal
Proceedings of SPIE (March 09 1999)

Back to Top