Prof. Hongping Li Profile

Prof. Hongping Li

at Ocean Univ of China

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Publications (2)

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Proceedings Article | 27 January 2021 Paper

Extreme low-light image enhancement using deep neural network

Ming Xu, Hongping Li, Jian Chen

Proceedings Volume 11720, 1172015 (2021) https://doi.org/10.1117/12.2589358

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Low-light images are inappropriate for human observation and computer processing. Multiple enhancement techniques have been proposed but most of them are based on synthetic datasets. In this paper, an image enhancement method using deep convolutional neural network is introduced. The model is expert at merging multi-scale information and it is flexible for image of arbitrary size without pre- or post-processing. Different from previous work, we deliberately address those extreme low-light images which cannot be identified by human eyes. For doing so, a Real Extreme Low-Light Images (RELLI) dataset is collected to validate the method, and will be contributed to related research. We also analyze some factors that affect the model’s performance to achieve the best results. Furthermore, the generalization ability beyond RELLI dataset and the superiority over several state-of-the-art methods are also confirmed.

Proceedings Article | 29 August 2016 Paper

Synthetic aperture lidar imaging through atmospheric turbulence

Tianan Lu, Hongping Li

Proceedings Volume 10033, 100333N (2016) https://doi.org/10.1117/12.2245077

KEYWORDS: LIDAR, Synthetic aperture radar, Atmospheric turbulence, Turbulence, Error analysis, Monte Carlo methods, Optical simulations, Phase shift keying, Radar imaging, Image resolution

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The resolution of a conventional optical imaging radar system is constrained by the diffraction limit of the telescope’s aperture. The combination of the lidar and synthetic aperture processing techniques can overcome the diffraction limit and provide a higher resolution air borne remote sensor. Atmospheric turbulence is an important factor affecting the lidar imaging, and phase screen simulation method is an effective method to simulate the degradation of laser signal propagating in turbulent atmosphere. This paper proposes a kind of phase screen generation method based on Monte-Carlo random factor. The expansion of phase data units can be selected by Monte-Carlo random factor which can increase the randomness of phase screens. The radar imaging with different turbulence intensity is also calculated in this paper, then the improved rank one phase estimation autofocus method is used to compensate the imaging phase errors. The results show that the method of generating phase screen is consistent with the statistics of atmospheric turbulence, which can well simulate the effect of atmospheric turbulence on synthetic aperture lidar, and the influence on synthetic aperture lidar azimuth resolution is greater when atmospheric turbulence is stronger. Improved rank one phase estimation algorithm has good autofocus effect, which can effectively compensate the phase errors and enhance the image quality degraded by turbulence.

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