﻿ 基于虚拟现实技术的低质量船舶三维图像重建
 舰船科学技术  2023, Vol. 45 Issue (20): 206-209    DOI: 10.3404/j.issn.1672-7649.2023.20.040 PDF

Research on low quality ship 3D image reconstruction based on virtual reality technology
ZHOU Lei
Henan University of Engineering, Zhengzhou 451191, China
Abstract: Image reconstruction is an important component in the field of image processing. To improve the quality of image reconstruction, a low-quality ship 3D image reconstruction method based on virtual reality technology is studied. In response to the problem of low image quality caused by external environment and collection equipment, the morphological filtering technology of virtual reality technology is used to preprocess the initial ship image. The ship image enhancement processing is achieved through dilation, corrosion, opening, and closing operations; Using exponential weight coefficients to describe ship image features; Obtain the minimum recognition distance of ship 3D images through virtual reality devices, determine the deviation between the theoretical image projection value and the actual projection value based on ship image features, and use the deviation value to calibrate the pixel values of ship image reconstruction to achieve high-precision ship 3D image reconstruction. The experimental results show that the method enhances the peak signal-to-noise ratio of the image by more than 14%, and the structural similarity of the reconstructed image reaches more than 95%, significantly improving the clarity.
Key words: virtual reality technology     low quality     3D image reconstruction     image enhancement     feature extraction     minimum recognition distance
0 引　言

1 低质量船舶三维图像重建方法 1.1 基于虚拟现实技术的低质量船舶图像预处理

 $f\left( {i,j} \right) \oplus g\left( {i,j} \right) = \max \left\{ {f\left( {i - 1,j - 1} \right) + g\left( {i,j} \right)} \right\}。$ (1)

 $f\left( {i,j} \right)\Theta g\left( {i,j} \right) = \max \left\{ {f\left( {i - x,j - y} \right) - g\left( {i,j} \right)} \right\}。$ (2)

 $\left\{ \begin{gathered} f\left( {i,j} \right) \circ g\left( {i,j} \right) = \left[ {f\left( {i,j} \right)\Theta g\left( {i,j} \right)} \right] \oplus g\left( {i,j} \right)，\\ f\left( {i,j} \right) \cdot g\left( {i,j} \right) = \left[ {f\left( {i,j} \right) \oplus g\left( {i,j} \right)} \right]\Theta g\left( {i,j} \right) 。\\ \end{gathered} \right.$ (3)

 图 1 数学形态学开启—闭合滤波器结构图 Fig. 1 Mathematical morphology opening closing filter structure diagram
1.2 基于指数权重系数的船舶图像特征提取

 $\Delta \left( {{A_f},H} \right) = \left\{ {{x_1},{x_2}, \cdots ,{x_n}} \right\}。$ (4)

1.3 基于虚拟现实技术的船舶图像三维重建

 $\phi = l\frac{{1.22\partial }}{D} \text{。}$ (5)

 ${f^{\left( {o + 1} \right)}} = {f^{\left( o \right)}} + {\sigma _o}{V^{ - 1}}{A_f} \cdot W\left( {p - {A_f}{f^{\left( o \right)}}} \right) 。$ (6)

 图 2 船舶三维图像重建过程流程图 Fig. 2 Flow chart of ship 3D image reconstruction process

 $\Delta i = {p_i} - \sum\limits_{n = 1}^N {{a_{in}}f_n^{\left( o \right)}}。$ (7)

2 实验结果与分析

2.1 图像处理性能测试

 图 3 本文方法图像增强效果 Fig. 3 Image enhancement effect of the method in this article

2.2 三维图像重建性能分析

3 结　语

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