Mr. Fang Liu Profile

Fang Liu

at Soochow Univ

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Publications (2)

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Proceedings Article | 28 February 2021 Paper

Imaging reconstruction through long-range scattering media by using deep learning

Bochao Zhang, Fang Liu, Baoxing Xiong, Fan Gao, Xiang Zhang, Xiao Yuan

Proceedings Volume 11781, 117811J (2021) https://doi.org/10.1117/12.2591332

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The realization of high-resolution imaging of images through scattering media has always been an important problem to be solved in the field. In this paper, our purpose here is to create a new framework that can realize the imaging through long-range scattering media. To do so, we establish a long-range scattering medium model, and use the model to generate simulated speckle pattern. In particular, we are designing a new neural network that is able to learn the statistical information found in the pattern of speckle intensity. The simulated speckle data were used as train sets for the neural network, and the learning rate of the SGD was 0.001, so that the model converged, which had good effects in the aspects of recovery time, imaging quality, mobility, convergence rate and so on. The peak signal to noise ratio (PSNR), Pearson correlation coefficient (PCC), structural similarity (SSIM) and other indexes were used to evaluate the performance of the convolution neural network in restoring images. Our neural network has achieved good results under this evaluation index from those results. PSNR value is 16.939, SSIM value is 0.842, and PCC value is 0.884, indicating that our new neural network model can realize long-range scattering media imaging and improve the imaging quality of scattering imaging.

Proceedings Article | 2 December 2020 Paper

New residual neural network for rapid imaging of scattering

Fang Liu, Bochao Zhang, Baoxing Xiong, Fan Gao, Xiang Zhang, Xiao Yuan

Proceedings Volume 11717, 117172G (2020) https://doi.org/10.1117/12.2587308

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There are various scattering media in nature. How to achieve rapid imaging through scattering media is an important issue in the field of optical imaging. In order to improve the speed of scattering imaging, this paper adopts a new type of residual neural network called XRNet, which mainly uses the advantages of easy optimization of residual neural network(ResNet) and combines U-Net structure and depthwise separable convolution instead of standard convolution to reduce the complexity of training model, thereby improving recovery speed of imaging. The image restoration speed and imaging quality under this model are simulated and calculated, and the convolutional neural network (CNN), ResNet and XRNet are analyzed by Pearson Correlation Coefficient (PCC), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The results show that XRNet has the fastest imaging speed when the imaging quality is basically unchanged. Finally, the imaging quality and recovery speed of XRNet at different depths are evaluated, and the best values are obtained at a depth of 31 layers.

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