A deep multi-layer image stitching framework for parallax scenes

Yan Hu; Rui Zhou

doi:10.1117/12.3035359

19 July 2024 A deep multi-layer image stitching framework for parallax scenes

Yan Hu, Rui Zhou

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130E (2024) https://doi.org/10.1117/12.3035359
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

The smooth warp field is becoming increasingly important in image stitching. When the 3D scene is confronted, the relative positions of the scene structures observed at different camera views may differ, thus causing parallax problems. Existing image stitching algorithms often fail in parallax scenes and are not efficient enough to meet the requirements of real-time tasks such as panoramic stitching for UAVs. In this paper, we propose a deep multilayer image stitching framework for parallax scenes based on GPU parallel computing, which includes multi-scale RANSAC layers, a parallax suppression layer and a smooth projection field generation layer. Instead of strictly dividing the RANSAC process and the dense warping model into two steps as in traditional image warping, we use the dense warping model for outlier filtering in the RANSAC process, and the two steps are performed in a cross iterative fashion. Experimental results show that our algorithm significantly outperforms the existing algorithms in terms of computational performance and achieves an approximate level of quality of stitching.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Hu and Rui Zhou "A deep multi-layer image stitching framework for parallax scenes", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130E (19 July 2024); https://doi.org/10.1117/12.3035359

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available