基于参数回归的快速全景图像拼接算法

郭璠; 李小虎; 刘文韬; 唐琎

doi:10.11959/j.issn.1000-436x.2023182

您当前的位置：

首页 >

文章列表页 >

基于参数回归的快速全景图像拼接算法

学术论文 | 更新时间：2024-06-06

- 基于参数回归的快速全景图像拼接算法
- Fast panoramic image stitching algorithm based on parameter regression
- 通信学报 2023年44卷第9期页码：36-47
- 作者机构：
  
  中南大学自动化学院，湖南长沙 410083
- 作者简介：
  
  [ "郭璠（1982- ），女，湖南临澧人，博士，中南大学副教授、硕士生导师，主要研究方向为图像处理、模式识别、人工智能等" ]
  [ "李小虎（1998- ），男，河南安阳人，中南大学硕士生，主要研究方向为计算机视觉、图像处理、模式识别等" ]
  [ "刘文韬（2000- ），男，湖北武汉人，中南大学硕士生，主要研究方向为计算机视觉、图像处理、模式识别等" ]
  [ "唐琎（1966- ），男，湖南武冈人，博士，中南大学教授、博士生导师，主要研究方向为计算机视觉、人工智能、机器人等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61502537);长沙市自然科学基金资助项目(kq2208286);湖南省自然科学基金资助项目(2023JJ30697)
- DOI：10.11959/j.issn.1000-436x.2023182
  中图分类号： TP391.41
- 网络出版日期：2023-09，
  
  纸质出版日期：2023-09-25
- 稿件说明：
移动端阅览
郭璠, 李小虎, 刘文韬, 等. 基于参数回归的快速全景图像拼接算法[J]. 通信学报, 2023,44(9):36-47.

Fan GUO, Xiaohu LI, Wentao LIU, et al. Fast panoramic image stitching algorithm based on parameter regression[J]. Journal on communications, 2023, 44(9): 36-47.
郭璠, 李小虎, 刘文韬, 等. 基于参数回归的快速全景图像拼接算法[J]. 通信学报, 2023,44(9):36-47. DOI： 10.11959/j.issn.1000-436x.2023182.

Fan GUO, Xiaohu LI, Wentao LIU, et al. Fast panoramic image stitching algorithm based on parameter regression[J]. Journal on communications, 2023, 44(9): 36-47. DOI： 10.11959/j.issn.1000-436x.2023182.

摘要

现实场景中照相机获得的图像视场角范围往往是有限的，而目前对全景图像的需求日益增大，因此针对拍摄得到的全景图像序列，提出了一种基于参数回归的快速全景图像拼接算法。将传统的图像配准任务转化为深度学习结合机器学习的方式，设计一种基于高斯差分金字塔的多尺度深度卷积神经网络（MDCNN）对待拼接图像进行特征提取，并使用LightGBM回归模型对拼接参数进行预测，获得图像之间的变换矩阵和照相机焦距完成图像对齐，并设计了一种双曲线图像融合算法消除图像之间的拼接缝。实验结果表明，所提算法能够实现图像的快速拼接，获得比已有代表性算法更清晰自然的全景拼接效果，同时对红外图像也具有很好的适应性。

Abstract

In reality

the field of view of images acquired by cameras was usually limited

and the demand for panoramic images was increasing.Therefore

a fast panoramic image stitching algorithm based on parameter regression was proposed for panoramic image sequences.The traditional image registration task was transformed into deep learning combined with machine learning

a multi-scale deep convolutional neural network (MDCNN) based on Gaussian difference pyramid was designed to extract features of stitching images

and LightGBM regression model was used to predict stitching parameters.The transformation matrix and the focal length of the camera were obtained to align the images

and a hyperbolic image fusion algorithm was designed to eliminate the stitching seam between the images.The experimental results show that the proposed algorithm can quickly mosaic images and obtain clearer and more natural panoramic mosaic effects than the existing representative algorithms.It also has good adaptability for infrared images.

关键词

Keywords

references

HARRIS C , STEPHENS M . A combined corner and edge detector [C ] // Proceedings of the Alvey Vision Conference 1988. [S.l. ] : Alvey Vision Club , 1988 : 147 - 151 .

SHU T . Multi-sensor remote sensing image alignment based on fast algorithms [J ] . Journal of Intelligent Systems , 2023 , 32 ( 1 ): 1 - 13 .

BROWN M , LOWE D G . Automatic panoramic image stitching using invariant features [J ] . International Journal of Computer Vision , 2007 , 74 ( 1 ): 59 - 73 .

ZARAGOZA J , CHIN T J , BROWN M S , et al . As-projectiveas-possible image stitching with moving DLT [C ] // Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2013 : 2339 - 2346 .

CHANG C H , SATO Y , CHUANG Y Y . Shape-preserving half-projective warps for image stitching [C ] // Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2014 : 3254 - 3261 .

LIN C C , PANKANTI S U , RAMAMURTHY K N , et al . Adaptive as-natural-as-possible image stitching [C ] // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2015 : 1155 - 1163 .

CHEN Y S , CHUANG Y Y . Natural image stitching with the global similarity prior [C ] // European Conference on Computer Vision . Berlin:Springer , 2016 : 186 - 201 .

曾国奇 , 牛子凡 , 郑丽丽 , 等 . 基于球形变换的无人机视频图像实时拼接方法 [J ] . 航空学报 , 2023 , 44 ( 23 ): 1 - 11 .

ZENG G Q , NIU Z F , ZHEN L L , et al . A real time video image stitching method for UAV based on spherical transformation [J ] . Acta Aeronautica et Astronautica Sinica , 2023 , 44 ( 23 ): 1 - 11 .

ZHU S D , ZHANG Y Z , ZHANG J , et al . ISGTA:an effective approach for multi-image stitching based on gradual transformation matrix [J ] . Signal,Image and Video Processing , 2023 , 17 ( 7 ): 3811 - 3820 .

KHAMIYEV I , ISSA D , AKHTAR Z , et al . Panoramic image generation using deep neural networks [J ] . Soft Computing , 2023 , 27 ( 13 ): 8679 - 8695 .

DAI Q Y , FANG F M , LI J C , et al . Edge-guided composition network for image stitching [J ] . Pattern Recognition , 2021 ,118:108019.

ZHANG H , ZHANG J M , PERAZZI F , et al . Deep image compositing [C ] // Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) . Piscataway:IEEE Press , 2021 : 365 - 374 .

LU C N , CHANG Y C , CHIU W C . Bridging the visual gap:wide-range image blending [C ] // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2021 : 843 - 851 .

PARIS S , KORNPROBST P , TUMBLIN J , et al . Bilateral filtering:theory and applications [J ] . Foundations and Trends in Computer Graphics and Vision , 2008 , 4 ( 1 ): 1 - 75 .

SHLENS J . A tutorial on principal component analysis [J ] . arXiv Preprint,arXiv:1404.1100 , 2014 .

FRIEDMAN J H . Greedy function approximation:a gradient boosting machine [J ] . The Annals of Statistics , 2001 , 29 ( 5 ): 1189 - 1232 .

CHEN T Q , GUESTRIN C . XGBoost:a scalable tree boosting system [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2016 : 785 - 794 .

KE G , MENG Q , FINLEY T , et al . LightGBM:a highly efficient gradient boosting decision tree [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York:ACM Press , 2017 : 3149 - 3157 .

FISCHLER M A , BOLLES R C . Random sample consensuss:a paradigm for model fitting with applications to image analysis and automated cartography [J ] . Communications of the ACM , 1981 , 24 ( 6 ): 381 - 395 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [J ] . Communications of the ACM , 2017 , 60 ( 6 ): 84 - 90 .

IANDOLA F N , HAN S , MOSKEWICZ M W , et al . SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and ＜0.5MB model size [J ] . arXiv Preprint,arXiv:1602.07360 , 2016 .

IANDOLA F , MOSKEWICZ M , KARAYEV S , et al . Densenet:Implementing efficient convnet descriptor pyramids [J ] . arXiv Preprint,arXiv:1404.1869 , 2014 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2016 : 770 - 778 .

MITTAL A , SOUNDARARAJAN R , BOVIK A C . Making a “completely blind” image quality analyzer [J ] . IEEE Signal Processing Letters , 2013 , 20 ( 3 ): 209 - 212 .

BLAU Y , MECHREZ R , TIMOFTE R , et al . The 2018 PIRM challenge on perceptual image super-resolution [C ] // European Conference on Computer Vision . Berlin:Springer , 2019 : 334 - 355 .

浏览量

344

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于全局-局部散度的多元时间序列无监督降维方法

面向6G的深度图像语义通信模型

DDAC：面向卷积神经网络图像隐写分析模型的特征提取方法

自编码器及其应用综述

基于信道冲激响应不敏感特征的分子通信非相干信号检测