Abstract
Recognition of images with low-resolution is extremely challenging, due to the feature smoothness caused by the loss of structural details. Specifically, after losing the structural details, low-resolution image patches with different structural properties tend to have a uniform distribution in the specific channels of deep representation space, which will introduce ambiguity for image recognition. To address this problem, this paper proposes a novel Feature Enhancement Module (FE-Module). The module first extracts similar features as the pre-trained classification networks. Then it captures features across different depths to make use of all the hierarchical features. Finally, the module explores the patches with similar structures to remedy local feature smoothness for accurate low-resolution image classification. Extensive experiment results demonstrate that the proposed method can effectively enhance the feature discrimination ability and improve recognition performance.
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Acknowledgements
This work was supported by the National Key R&D Program of China under Grand 2020AAA0105702, National Natural Science Foundation of China (NSFC) under Grants U19B2038, the University Synergy Innovation Program of Anhui Province under Grants GXXT-2019-025 and the key scientific technological innovation research project by Ministry of Education.
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Kang, S., Wang, Y., Cao, Y., Zha, ZJ. (2022). Long-Range Feature Dependencies Capturing for Low-Resolution Image Classification. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_1
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DOI: https://doi.org/10.1007/978-3-030-98355-0_1
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