﻿ 视觉传达技术的舰船图像三维重构研究
 舰船科学技术  2022, Vol. 44 Issue (8): 161-164    DOI: 10.3404/j.issn.1672-7649.2022.08.034 PDF

Research on 3D reconstruction of ship image based on visual communication technology
WU Xing-bo
Science and Technology College, Gannan Normal University, Ganzhou 341000, China
Abstract: Aiming at the problem that the current 3D reconstruction technology is affected by illumination, a research method of 3D reconstruction of ship image based on visual communication technology is proposed. Threshold segmentation method is used to extract contour features of ship image. Combined with fuzzy processing method, texture feature gradient decomposition is used to construct 3D image visual communication model. Using visual feature extraction technology to obtain spatial information features, a network distribution recombination model is constructed to match image feature points. Combined with the dynamic interaction mechanism of 3D image visual communication, the spatial information of 3D image is reconstructed to obtain the distribution set of texture features. The image denoising process is combined with smooth filtering method to obtain 3D image reconstruction results. Use visual communication technology to realize image enhancement and avoid light interference. The experimental results show that the 3D point coordinates of this method are basically consistent with the ideal situation, and the high-resolution image reconstruction results can be obtained with the highest SNR of 34dB, indicating that the reconstruction effect of this method is good.
Key words: visual communication     ship image     three-dimensional reconstruction     image enhancement
0 引　言

1 基于视觉传达技术的舰船图像三维重构 1.1 舰船图像轮廓特征提取与数据处理

 g(x,y) = \left\{ \begin{aligned} &0，\quad &f(x,y) < U，\\ &f(x,y) ，&f(x,y) \geqslant U。\end{aligned} \right. (1)

1.2 舰船图像视觉传达模型构建

 $I(x,y) = \frac{1}{{\delta (x)}}\exp \left( { - \frac{{d(x,y)}}{{{z^2}}}} \right) 。$ (2)

 $\varepsilon = \vartheta \cdot \frac{J}{{{A^ * }}} + (1 - \vartheta ) 。$ (3)

1.3 基于三维图像网格分布重组的图像特征点匹配

 $q = \partial S + L(\phi ) + P(\phi ) 。$ (4)

 图 1 网络分布重组模型 Fig. 1 Network distribution recombination model

 $similar\left( {{I_1},{I_2}} \right) = \frac{{\left[ {{I_1}p(o,v) - \overline {{I_1}((o,v))} } \right]}}{{\varsigma \sqrt {{\phi _I}_1 \times {\phi _I}_2} }} 。$ (5)

1.4 三维图像虚拟重构输出

 图 2 三维图像视觉传达动态交互 Fig. 2 Dynamic interaction of 3D image visual communication

 ${z_1}\left[ {\begin{array}{*{20}{l}} {{d_1}} \\ {{h_1}} \\ 1 \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} {s_{11}^1,s_{12}^1,s_{13}^1,s_{14}^1} \\ {s_{21}^1,s_{22}^1,s_{23}^1,s_{24}^1} \\ {s_{31}^1,s_{32}^1,s_{33}^1,s_{34}^1} \end{array}} \right]\left[ {\begin{array}{*{20}{l}} {{x_w}} \\ {{y_w}} \\ {{z_w}} \\ 1 \end{array}} \right]。$ (6)

 $f=\underset{\text{Δ}x\to 0}{\mathrm{lim}}\left[\frac{\alpha -\text{Δ}\alpha }{\text{Δ}x}\right] 。$ (7)

 $g = \left\langle {i,{j_0}} \right\rangle {j_0} 。$ (8)

2 基于视觉传达技术的图像增强处理

 图 3 基于视觉传达技术的图像增强过程 Fig. 3 Image enhancement process based on visual communication technology

 $W(r,u) = \frac{1}{{\sqrt r }}\delta \left( {\frac{{t - u}}{r}} \right){\rm{d}}t 。$ (9)

 $W'(r,u) = \displaystyle\int\nolimits f(t)\delta '(t){\text{d}}t 。$ (10)

3 实验结果与分析

 图 4 3种方法舰船图像三维重构效果对比分析 Fig. 4 Comparative analysis of 3D reconstruction effects of ship images by three methods

 图 5 3种方法信噪比对比分析 Fig. 5 Comparative analysis of SNR of the three methods
4 结　语

 [1] 宋燕飞, 罗尧治, 沈雁彬, 等. 基于双目视觉与图像识别的网架结构三维重建[J]. 空间结构, 2020, 26(4): 28-35+74. [2] 尹燕运, 师影. 计算机视觉技术在交通工程测量中的应用[J]. 工程技术研究, 2020, 61(5): 96-97. DOI:10.3969/j.issn.1671-3818.2020.05.048 [3] 林平, 李琦, 申作春. 连续场景太赫兹数字全息三维重建图像的参数影响[J]. 激光与光电子学进展, 2020, 57(22): 49-57. [4] 孙克强, 缪君, 江瑞祥, 等. 基于空洞卷积与多尺度特征融合的室内场景单图像分段平面三维重建[J]. 传感技术学报, 2021, 34(3): 370-378. DOI:10.3969/j.issn.1004-1699.2021.03.012 [5] 冯维, 汤少靖, 赵晓冬, 等. 基于自适应条纹的高反光表面三维面形测量方法[J]. 光学学报, 2020, 40(5): 119-127. [6] 邢志勇, 肖儿良, 简献忠. 双判别生成对抗网络的红外图像超分辨重建[J]. 小型微型计算机系统, 2020, 41(03): 662-667. DOI:10.3969/j.issn.1000-1220.2020.03.036 [7] 万书亭, 张伯麟, 尹涛, 等. X射线无损检测图像三维重建软件设计[J]. 中国工程机械学报, 2020, 18(5): 425-429+435. [8] 张豪, 张强, 邵思羽, 等. 深度学习在单图像三维模型重建的应用[J]. 计算机应用, 2020, 40(8): 2351-2357.