Prof. Ronghua Li Profile

Prof. Ronghua Li

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SPIE Journal Paper | 19 June 2023

Underwater polarization image restoration based on a partition method

Ronghua Li, Shenghui Zhang, Changye Cai, Yunhe Xu, Haotian Cao

OE, Vol. 62, Issue 06, 068103, (June 2023) https://doi.org/10.1117/12.10.1117/1.OE.62.6.068103

KEYWORDS: Polarization, Image restoration, Reflection, Image filtering, Image quality, Tunable filters, Image processing, Polarized light, Polarization imaging, Gaussian filters

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The quality of underwater polarization imaging is mainly affected by the polarization properties of the target and impurity particles. Traditional methods often assume uniform polarization characteristics of the target, which make it difficult to address the restoration issues of complex targets. In response, a partition-based method for recovering underwater polarization images is proposed. The method involves pre-processing the image using the Gaussian curvature filtering algorithm, partitioning the image based on polarization information. In addition, a joint image evaluation method is used to achieve restoration of complex polarized characteristic targets. The method estimates the value of the reflected light polarization of one partition to estimate the value of the next partition and links the polarization values of each partition. Our approach achieves clear restoration results for multiple targets or complex structural objects underwater. Achieving significant improvement in image quality in multi-target underwater scenes, our method is highly effective for complex underwater environments. Experimental results show that our method, when compared with three other newer methods on multiple images of different targets and under varying scattering conditions, achieves an average increase of 617% in the standard deviation image evaluation index and a 61% optimization in the natural image quality evaluator index. Furthermore, our method is robust for different degrees of water turbidity.

Proceedings Article | 17 April 2020 Paper

Rough registration method for point cloud of spatial non-cooperative target by improving FPFH

Rong-hua Li, Bo Yang, Qi Lu

Proceedings Volume 11455, 114552I (2020) https://doi.org/10.1117/12.2563868

KEYWORDS: Clouds, Detection and tracking algorithms, Radar, Principal component analysis, 3D acquisition, Image registration, 3D modeling, LIDAR, Space operations, Data modeling

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It has become an important and difficult point to solve the problem of non-cooperative target pose measurement in space. The three-dimensional point cloud data of the target acquired by the laser radar can restore its three-dimensional morphology through point cloud registration, so as to solve the relative pose of non-cooperative target in space. In view of the fact that the traditional point cloud Iterative Closest Point (ICP) registration algorithm is prone to fall into local extremum when the initial value is not good, which leads to registration failure, a rough registration algorithm of 3D point cloud based on the combination of bounding box and FPFH descriptor using principal component analysis is proposed. In this method, PCA is used to construct a three-dimensional point cloud bounding box. The parameter threshold of FPFH describing sub-point cloud on-time matching is selected through the density of point cloud and the outer dimension of outer bounding box. The normal alignment is added to adjust the normal direction of point cloud. The experimental results show that the proposed method simplifies the complexity feature parameters selection, reduces the search range of the feature point matching, and provides the error of precise registration of about 3°, to solve the spatial non-cooperative target relative position measurement technology provides technical reference.

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