A robust feature matching algorithm based on adaptive feature fusion combined with image superresolution reconstruction

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 17-18; pp. 8576 - 8591
• Main Authors: Huangfu, Wenjun, Ni, Cui, Wang, Peng, Zhang, Yingying
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2024; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Artificial neural networks; Computer Science; Feature extraction; Image enhancement; Image reconstruction; Image resolution; Machine learning; Machines; Manufacturing; Matching; Mechanical Engineering; Processes; Feature matching; Feature fusion; Adaptive threshold; Superresolution reconstruction
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-024-05600-0

With the development of image feature matching technology, feature matching algorithms based on deep learning have achieved excellent results, but in scenarios with low texture or extreme perspective changes, the matching accuracy is still difficult to guarantee. In this paper, a superresolution reconstruction method based on a Residual-ESPCN (efficient subpixel convolutional neural network) approach is proposed based on LoFTR (local feature matching with transformers). The superresolution method is used to improve the interpolation method used in ASFF (adaptive spatial feature fusion) to increase the image resolution, enhance the detailed information of the image, and make the extracted features richer. Then, ASFF is introduced into the local feature extraction module of LoFTR, which can alleviate the inconsistency problem of information transmission between different scale features of the feature pyramid and lessen the amount of information lost during transmission from low- to high-resolution levels. Moreover, to improve the adaptability of the algorithm to different scenarios, OTSU is introduced to adaptively calculate the threshold of feature matching. The experimental results show that in different indoor or outdoor scenarios, our proposed algorithm for matching features can effectively improve the adaptability of feature matching and can achieve good results in terms of the area under the curve (AUC), accuracy and recall. Graphical Abstract