面向激光光条图像修复的循环相似度映射网络

冀振燕; 韩梦豪; 宋晓军; 冯其波

doi:10.12263/DZXB.20211538

您当前的位置：

首页 >

文章列表页 >

面向激光光条图像修复的循环相似度映射网络

学术论文 | 更新时间：2025-12-08

- 面向激光光条图像修复的循环相似度映射网络
- Recurrent Similarity Mapping Network Oriented to Laser Stripe Image Inpainting
- 电子学报 2022年50卷第5期页码：1234-1242
- 作者机构：
  
  1.北京交通大学软件学院，北京 100044
  2.北京交通大学理学院，北京 100044
- 作者简介：
  
  [ "冀振燕女，1972年4月出生，河南人.博士，现为北京交通大学软件学院副教授，博士生导师.主要研究领域为计算机视觉、推荐系统等.E-mail: zhyji@bjtu.edu.cn" ]
  [ "韩梦豪男，1999年5月出生，山西人.现为北京交通大学软件学院硕士研究生.主要研究方向为计算机视觉、深度学习等." ]
  [ "宋晓军男，1995年9月出生，山东人.研究生，毕业于北京交通大学软件学院.主要研究方向为计算机视觉、深度学习等." ]
  [ "冯其波男，1962年5月出生，广东人.博士，现为北京交通大学理学院教授，博士生导师.研究方向为光学测量、铁路安全测量技术、仪器仪表、计算机视觉等." ]
- 基金信息：
  
  国家自然科学基金面上项目(52175493);国家自然科学基金重点项目(51935002)
- DOI：10.12263/DZXB.20211538
  中图分类号： TP183
- 收稿：2021-11-17，
  
  修回：2022-03-13，
  
  纸质出版：2022-05-25
- 稿件说明：
移动端阅览
冀振燕,韩梦豪,宋晓军等.面向激光光条图像修复的循环相似度映射网络[J].电子学报,2022,50(05):1234-1242.

JI Zhen-yan,HAN Meng-hao,SONG Xiao-jun,et al.Recurrent Similarity Mapping Network Oriented to Laser Stripe Image Inpainting[J].ACTA ELECTRONICA SINICA,2022,50(05):1234-1242.
冀振燕,韩梦豪,宋晓军等.面向激光光条图像修复的循环相似度映射网络[J].电子学报,2022,50(05):1234-1242. DOI： 10.12263/DZXB.20211538.

JI Zhen-yan,HAN Meng-hao,SONG Xiao-jun,et al.Recurrent Similarity Mapping Network Oriented to Laser Stripe Image Inpainting[J].ACTA ELECTRONICA SINICA,2022,50(05):1234-1242. DOI： 10.12263/DZXB.20211538.

摘要

基于线结构光的视觉检测技术广泛应用于工业检测，现场动态采集的激光光条图像通常含有光斑和局部断裂，影响光条中心提取精度进而影响测量精度，因此，需要构建图像修复模型去除光斑修复断裂.现有图像修复模型在RGB数据集上的修复效果显著，但不适用于激光光条灰度图像的修复.因此本文提出面向激光光条图像修复的循环相似度映射网络（Recurrent Similarity Mapping Network，RSM-Net），以循环网络为主体，采用软编码Pconv（Partial convolution）层取代部分原始Pconv层，强化特征学习能力；设计非对称相似度模块，降低图像背景特征对修复的负面影响；设计含有多尺度结构相似性（Multi-Scale Structural SIMilarity，MS-SSIM）损失项的混合损失函数，精确地引导光条结构信息的还原，实现高精度的激光光条图像修复.实验验证RSM-Net在小光斑区域、大光斑区域和断裂区域的修复精度均优于所对比的主流图像修复模型.

Abstract

The vison inspection technology based on line structured light is broadly used in industrial inspection. The images dynamically collected onsite usually contain stripe adhesions and local fractures

which influences the center line extraction accuracy and further measurement accuracy. Thus

it is necessary to construct an image inpainting model to remove the stripe adhesions and fix the local fractures. The existing image inpainting models can achieve high accuracy on RGB datasets

but cannot adapt to laser stripe grayscale images. Therefore

a laser stripe image inpainting network RSM-Net(Recurrent Similarity Mapping Network) is proposed. The kernel of RSM-Net is a recurrent neural network. RSM-Net replaces some original Pconv(Partial convolution) layers with soft-coding Pconv layers to strengthen the feature learning ability. The asymmetric similarity module is designed to decrease the negative impact of image background features on the restoration. A mixed loss function containing the multi-scale structural similarity(MS-SSIM) loss term is designed to precisely guide the restoration of stripe structural information and realize the high-precision laser stripe image inpainting. The experiments demonstrate that the proposed RSM-Net outperforms the compared state-of-the-art image inpainting models in inpainting accuracy for small and large stripe adhesions

and local fractures.

关键词

Keywords

references

BERTALMIO M , SAPIRO G , CASELLES V , et al . Image inpainting [C]// Proceedings of the 27th Annual Conferenc on Computer graphics and interactive techniques , New York : ACM , 2000 : 417 - 424

高成英 , 徐仙儿 , 罗燕媚 , 等 . 基于稀疏表示的物体图像修复 [J]. 计算机学报 , 2019 , 42 ( 9 ): 1953 - 1965 .

GAO C Y , XU X E , LUO Y M , et al . Object image inpainting based on sparse representation [J]. Chinese Journal of Computers , 2019 , 42 ( 9 ): 1953 - 1965 . (in Chinese)

CRIMINISI A , PÉREZ P , TOYAMA K . Region filling and object removal by exemplar-based image inpainting [J]. IEEE Transactions on Image Processing , 2004 , 13 ( 9 ): 1200 - 1212 .

CHENG W H , HSIEH C W , LIN S K , et al . Robust algorithm for exemplar-based image inpainting [C]// Proceedings of International Conference on Computer Graphics, Imaging and Visualization . Beijing : IEEE , 2005 : 64 - 69 .

PATHAK D , KRÄHENBÜHL P , DONAHUE J , et al . Context encoders: feature learning by inpainting [C]// 2016 IEEE conference on Computer Vision and Pattern Recognition . Las Vegas, NV : IEEE , 2016 : 2536 - 2544 .

IIZUKA S , SIMO-SERRA E , ISHIKAWA H . Globally and locally consistent image completion [J]. ACM Transactions on Graphics(ToG) , 2017 , 36 ( 4 ): 1 - 14 .

SAGONG M C , SHIN Y G , KIM S W , et al . PEPSI: fast image inpainting with parallel decoding network [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA : IEEE , 2019 : 11352 - 11360 .

LIU G , REDA F A , SHIH K J , et al . Image inpainting for irregular holes using partial convolutions [C]// Proceedings of the European conference on computer vision (ECCV) . Munich : Springer , 2018 : 85 - 100 .

ZHENG C X , CHAM T J , CAI J F . Pluralistic Image Completion [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA : IEEE , 2019 : 1438 - 1447 .

YU J H , LIN Z , YANG J M , et al . Free-form image inpainting with gated convolution [C]// 2019 IEEE/CVF International Conference on Computer Vision(ICCV) . Seoul : IEEE , 2019 : 4470 - 4479 .

XIE C H , LIU S H , LI C , et al . Image inpainting with learnable bidirectional attention maps [C]// 2019 IEEE/CVF International Conference on Computer Vision(ICCV) . Seoul : IEEE , 2019 : 8857 - 8866 .

XIONG W , YU J H , LIN Z , et al . Foreground-aware image inpainting [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA : IEEE , 2019 : 5833 - 5841 .

NAZERI K , NG E , JOSEPH T , et al . Edgeconnect: structure guided image inpainting using edge prediction [C]// 2019 IEEE/CVF International Conference on Computer Vision Workshops(ICCVW) . Seoul : IEEE , 2019 : 3265 - 3274 .

WANG M H , CHEN X M , CHEN W Q , et al . MSMC-net: Image inpainting using deep multi-scale and multi-connection networks [C]// 2019 IEEE International Conference on Multimedia & Expo Workshops . Shanghai : IEEE , 2019 : 683 - 686 .

RONNEBERGER O , FISCHER P , BROX T . U-net: Convolutional networks for biomedical image segmentation [C]// Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015 . Munich : Springer , 2015 : 234 - 241 .

LI J Y , WANG N , ZHANG L F , et al . Recurrent feature reasoning for image inpainting [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Seattle, WA : IEEE , 2020 : 7757 - 7765 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向时序异常检测的可变视距多向扫描方法

基于稀疏平滑自蒸馏的差分隐私深度学习方法

基于非一般类算子融合方法及硬件架构设计

基于注意力融合多尺度特征的解压缩点云质量增强方法