Abstract
Social media network is inseparable from image recognition, and image super-resolution (SR) reconstruction plays an important role in image recognition. The changes of scale and geometry are rarely considered in the image super-resolution reconstruction based on deep learning over the years, we introduce a super-resolution reconstruction network based on deformable convolutional network. We replace the ordinary convolution with the deformable convolution to pretend the geometric deformation and extract abundant local features. The image super-resolution reconstruction is usually based on the conventional convolutional neural network (CNN). Most CNN-based SR models do not utilize the features of the original low resolution (LR) image as much as possible, resulting in lower performance. After introducing the idea of deformable convolution, though the complexity is increased, the recognition accuracy is obviously raised.
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Acknowledgements
This paper was supported in part by the National Natural Science Foundation of China under Grant 61702110, and Grant 61972102, in part by the Natural Science Foundation of Guangdong Province under Grant 2019A1515011811, in part by the Research and Development Program of Guangdong Province under Grant 2020B010166006, in part by the Guangzhou Science and technology plan project under Grant 202002030110.
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Li, C., Fei, L., Qin, J., Liu, D., Teng, S., Zhang, W. (2021). Image Super-Resolution Using Deformable Convolutional Network. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_48
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