[1]姚莉,杜俊康,李长顺.针对伪影改善的图像拼接方法[J].郑州大学学报(工学版),2021,42(01):35-41.[doi:10.13705/j.issn.1671-6833.2021.01.006]
　YAO Li,DU Junkang,LI Changshun.Image Stitching Method for Improvement of Artifact[J].Journal of Zhengzhou University (Engineering Science),2021,42(01):35-41.[doi:10.13705/j.issn.1671-6833.2021.01.006]
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针对伪影改善的图像拼接方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年01期
页码:
35-41
栏目:
出版日期:
2021-03-14

文章信息/Info

Title:
Image Stitching Method for Improvement of Artifact
作者:
姚莉; 杜俊康; 李长顺
东南大学计算机科学与工程学院;

Author(s):
YAO Li;  DU Junkang;  LI Changshun
School of Computer Science and Engineering, Southeast University, Nanjing 211102, China
关键词:
Keywords:
image stitching;  artifact;  grid optimization;  image registration
分类号:
TP37
DOI:
10.13705/j.issn.1671-6833.2021.01.006
文献标志码:
A
摘要:
针对图像拼接中的伪影问题，提出了一整套的拼接解决方案。图像拼接方法分为两大阶段：图像配准阶段和图像融合阶段。在图像配准阶段，提出了一种基于网格优化的图像配准方法。通过使用点特征和线特征相结合的特征提取算法，提高特征的数量和质量。同时提出了基于先验的RANSAC特征点对筛选方法，剔除误匹配点对，并提高了模型计算的速度。为了进一步提升对齐效果和优化伪影问题，提出了多项图像对齐约束。最后采用了柱面投影模型，将图像对投影到同一平面。在图像融合阶段，使用了基于图像对重叠区域细节增强的缝合线检测算法，优化多焦段场景下的拼接效果。利用渐入渐出融合算法对同一平面的投影图像进行融合，得到一幅宽视野的高分辨率图像拼接结果。通过将现有的拼接方法和本文方法进行实验数据集验证对比，结果表明本文的方法能够更好地应对伪影问题，在拼接质量上有着更优的表现。
Abstract:
This paper focused on solving the problem of artifact in image mosaic technology, and proposed a whole set of mosaic scheme to solve the problem. Image stitching method was divided into two stages: image registration stage and image fusion stage. In the stage of image registration, in this paper, an image registration method based on grid optimization was proposed. By using the feature extraction algorithm combining point feature and line feature, the quantity and quality of features could be improved. At the same time, a prior screening method of RANSAC feature point pairs was proposed, which could eliminate mismatching point pairs and improve the speed of model calculation. In order to further improve the alignment effect and optimize the artifacts, a number of image alignment constraints were proposed. Finally, the cylinder projection model was used to project the image pairs to the same plane. In the stage of image fusion, a seam-line detection algorithm based on the enhancement of the details of the overlapping areas of the image was used to optimize the stitching effect in the multi-focus scene. The gradual image fusion algorithm was used to fuse the projection image of the same plane, and a wide field of vision high-resolution image mosaic result was obtained. The existing splicing methods were compared with the method in this paper. The results showed that the methods in this paper maintained the PSNR value verified in multiple scenarios in the range of 35-40 dB with low distortion, which could deal with the problem of artifacts better, and could have better performance in image stitching quality.

参考文献/References:

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

[2] GAO J, KIM S J, BROWN M S. Constructing image panoramas using dual-homography warping[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2011: 49-56.
[3] ZARAGOZA J, CHIN T J, Tran Q H, et al. As-projective-as-possible image stitching with moving DLT[J]. IEEE transactions on pattern analysis and machine intelligence, 2014, (36)7:1285-1298.
[4] CHEN Y S,CHUANG Y Y.Natural image stitching with the global similarity prior[C]//European Conference on Computer Vision. Amsterdam, The Netherlands: ECCV, 2016: 186-201.
[5] MISRA I, SHRIVASTAVA A, GUPTA A, et al. Cross-stitch networks for multi-task learning[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 3994-4003.
[6] GAO J, LI Y, CHIN T J, et al. Seam-driven image stitching [C]//European Association for Computer Graphics. Geneva, Switzerland: Eurographics, 2013: 45-48.
[7] ZHANG F, LIU F. Parallax-tolerant image stitching[C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York:IEEE, 2014: 3262-3269.
[8] LIN K M,JIANG N J,CHEONG L F,et al. Seagull: seam-guided local alignment for parallax-tolerant image stitching[C]// European Conference on Computer Vision. Berlin:Springer,2016: 370-385.
[9] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2): 91-110.
[10] VON GIOI R G,JAKUBOWICZ J,MOREL J M,et al.LSD:a line segment detector[J].Image processing on line,2012,2:35-55.
[11] 齐向明,王佳琦.网格形变细分的大视差图像拼接算法[J].计算机工程,2020,46(1):236-242.
[12] LIN K, JIANG N J, LIU S C. Direct photometric alignment by mesh deformation[C]// IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE, 2017: 2701-2709.
[13] ZHANG Y, LAI Y K, ZHANG F L. Stereoscopic image stitching with rectangular boundaries[J]. The visual computer, 2019, 35(6/7/8): 823-835.
[14] Lin C C, PANKANTI S U, RAMAMURTHY K N, et al. Adaptive as-natural-as-possible image stitching[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 1155-1163.

更新日期/Last Update: 2021-03-15