Ms. Ying Xue Profile

Ying Xue

at PLA Army Academy of Artillery and Air Defense

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Proceedings Article | 27 January 2021 Paper

Low-light image enhancement via layer decomposition and optimization

Ying Xue, Pucheng Zhou, Mogen Xue

Proceedings Volume 11720, 117201B (2021) https://doi.org/10.1117/12.2589353

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When capturing images in low-light conditions, the images are often degraded with low visibility and severe noise. To improve the visual quality and repress the noise simultaneously, a kind of low-light image enhancement method via layer decomposition and optimization was proposed. Firstly, the low-light image was smoothed via iterative least squares thus we could get the noise-free basic layer. Secondly, by means of subtraction of the original image and the basic layer we could get the detailed layer. Then we enhance the basic layer via variational Retinex-based method. At the meantime, we weaken the noise of the detailed layer by non-subsampled shearlet transform. Finally, we could obtain the enhanced image by fusion of the optimized basic layer and detailed layer. Experimental results of a number of low-light images reveal the efficiency of the proposed method and show its superiority over several state-of-the-arts.

Proceedings Article | 17 April 2020 Paper

Target detection method for polarization imaging based on convolutional neural network

Ruichao Xie, HongYu Zu, Ying Xue, RongChang Wang, Yong Wang

Proceedings Volume 11455, 114557Z (2020) https://doi.org/10.1117/12.2565375

KEYWORDS: Polarization, Target detection, Convolutional neural networks, Detection and tracking algorithms, Image processing

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The details and shape information of the target are effectively highlighted in the polarized image, which is more conducive to target detection. At present, the influence of different polarization parameters on the target detection task has not been studied in depth. There are problems that the ways of characterization of polarization parameter is so rich and varied that the polarization parameter is difficult to select, when we utilize the convolutional neural network (CNN) model to detect images obtained by polarimetric systems. In response to this problem, this paper uses the convolutional neural network (CNN) model to train a variety of polarized parametric images in many different input configuration for experimental comparison. Firstly, the sample data is acquired using a focal plane polarized camera, and the database is expanded using a data enhancement strategy to establish a polarized image data set. Then, different image input configurations are used as the training set, and the convolutional neural network (CNN) pre-training model is iteratively trained and fine-tuned to obtain the target detection model of the polarized image. Finally, in order to evaluate the performance of the model, the experimental trials are quantified by mean average precision (mAP) and processing time, and the influence of different polarization image input configurations on the detection model is analyzed. The experimental results show that compared with the model trained by single channel image configuration, the model trained by threechannel image configuration has better performance, but there is no obvious difference between models trained by different three-channel configurations.

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