Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation  

作　　者：Chunbin Qin Xiaotian Ran 

机构地区：[1]School of Artificial Intelligence,Henan University,Zhengzhou,450000,China

出　　处：《Computers, Materials & Continua》2024年第4期1319-1334,共16页计算机、材料和连续体（英文）

基　　金：Science and Technology Research Project of the Henan Province(222102240014).

摘　　要：Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

关 键 词：Unsupervised image stitching deep homography estimation YOLOv8 attention mechanism 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]