A robust feature matching algorithm based on adaptive feature fusion combined with image superresolution reconstruction
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 rec...
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| Published in | Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 17-18; pp. 8576 - 8591 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.09.2024
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0924-669X 1573-7497 |
| DOI | 10.1007/s10489-024-05600-0 |
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| Abstract | 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 |
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| AbstractList | 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. 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 |
| Author | Wang, Peng Zhang, Yingying Huangfu, Wenjun Ni, Cui |
| Author_xml | – sequence: 1 givenname: Wenjun surname: Huangfu fullname: Huangfu, Wenjun organization: School of Information Science and Electrical Engineering, Shandong Jiaotong University – sequence: 2 givenname: Cui surname: Ni fullname: Ni, Cui email: emilync@126.com organization: School of Information Science and Electrical Engineering, Shandong Jiaotong University – sequence: 3 givenname: Peng surname: Wang fullname: Wang, Peng email: knightwp@126.com organization: School of Information Science and Electrical Engineering, Shandong Jiaotong University, Institute of Automation, Shandong Academy of Sciences – sequence: 4 givenname: Yingying surname: Zhang fullname: Zhang, Yingying organization: School of Information Science and Electrical Engineering, Shandong Jiaotong University |
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| SubjectTerms | 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 |
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| Title | A robust feature matching algorithm based on adaptive feature fusion combined with image superresolution reconstruction |
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