Mr. Gangming Liang Profile

Gangming Liang

at Xidian Univ

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Proceedings Article | 10 April 2018 Paper

PMSR model for low illumination image enhancement

Yong Li, Junping Wang, Gangming Liang, Jiajia Guo

Proceedings Volume 10615, 106152T (2018) https://doi.org/10.1117/12.2303371

KEYWORDS: Image enhancement, Image processing, Gaussian filters, Image information entropy

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Enhancement of low illumination images is of great importance in poor imaging conditions. A new image enhancement model was proposed in this paper. The model divided an image into blocks and used the local standard deviation to design the center/surround filter and utilized amplitude compensation factor to compensate the shortage of logarithmic function in compressing the near-zero data’s amplitude. In addition, the amplitude compensation factor can suppress noise. At the same time, the normalized brightness can maintain the normal brightness region of the image while the brightness of the image is increased. In order to verify the performance of the proposed model, the proposed model and existing models are applied to image enhancement. To evaluate its performance in image enhancement, results are compared from the subjective and objective aspects. The experimental results show that the proposed model preserved the image details better and avoided the excessive enhancement of the normal brightness region.

Proceedings Article | 21 July 2017 Paper

New grayscale morphological operators on hypergraph

Junping Wang, Gangming Liang, Yahui Zheng, Yao Wu

Proceedings Volume 10420, 1042023 (2017) https://doi.org/10.1117/12.2281660

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New grayscale morphological operators on hypergraph are proposed to avoid the loss of details caused by fixed structure element effectively. Hypergraph, the most general structure in discrete mathematics, is also a subset of a finite set. Being a structured representation of information, the ordinary image can be transformed into a hypergraph model, which can integrate hypergraph theory with mathematical morphology theory. Because hypergraphs have good performance in structuring information, first of all, this paper designs a reasonable method of turning grayscale images into hypergraph space. Then based on hypergraph theory, new grayscale morphological operators on hypergraph are defined. Experiments show that using the new operators can avoid the loss of image detail information, and improve the precision of image processing.

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