基于偏振图像与粗糙深度图的形状重建

doi:10.13438/j.cnki.jdzk.2022.04.005

吉首大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (4): 20-26.DOI: 10.13438/j.cnki.jdzk.2022.04.005

基于偏振图像与粗糙深度图的形状重建

周梦园，杨奋林

(吉首大学数学与统计学院，湖南吉首 416000)

出版日期:2022-07-25 发布日期:2022-10-28
通讯作者: 杨奋林（1982—），女，湖南宁乡人，吉首大学数学与统计学院副教授，博士，主要从事图像处理和科学计算研究.
作者简介:周梦园（1999—），女，湖南永顺人，吉首大学数学与统计学院硕士研究生，主要从事图像重建研究

Shape Reconstruction Based on Polarization and Coarse Depth Images

ZHOU Mengyuan,YANG Fenlin

(School of Mathematics and Statistics,Jishou University,Jishou 416000,Hunan China)

Online:2022-07-25 Published:2022-10-28

摘要/Abstract

摘要：首先使用双边滤波法对粗糙深度图作平滑处理，该方法在保持边缘细节的同时可以很好地去噪；然后针对偏振法线的歧义问题，以深度信息作为先验条件，采用自定义阈值分割方法校正；最后构造以深度约束为拟合项、偏振法线约束为光滑项的罚函数方法，并利用最小二乘法进行计算，以实现融合重建.选取4幅不同偏振角度的偏振图像与单幅粗糙深度图进行实验，结果表明，相对于单一模态，深度与偏振的多模态融合能恢复更好的表面细节，达到更优的几何形状效果.

关键词： 偏振, 粗糙深度图, 双边滤波, 多模态融合, 阈值分割

Abstract: Firstly,the coarse depth image is smoothed by bilateral filtering,which can remove noise well while keeping edge details.Then,the depth information is taken as a prior condition to correct the ambiguity of polarization normals by using a self-defined threshold segmentation method.Finally,a penalty function method with depth constraint as the fitting term and polarization normal constraint as the smooth term is constructed,and the least square method is used to calculate the fusion reconstruction.Four polarization images with different polarization angles and a coarse depth map are selected for experiments.The experimental results show that compared with single mode,the fusion of depth and polarization can recover better surface details and achieve better geometric shape effect.

Key words: polarization, coarse depth map, bilateral filtering, multimodal fusion, threshold segmentation

周梦园, 杨奋林. 基于偏振图像与粗糙深度图的形状重建[J]. 吉首大学学报（自然科学版）, 2022, 43(4): 20-26.

ZHOU Mengyuan, YANG Fenlin. Shape Reconstruction Based on Polarization and Coarse Depth Images[J]. Journal of Jishou University(Natural Sciences Edition), 2022, 43(4): 20-26.

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基于偏振图像与粗糙深度图的形状重建

Shape Reconstruction Based on Polarization and Coarse Depth Images

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