Abstract
In recent years, deep learning has achieved great success in the field of image processing. In the single image super-resolution (SISR) task, the convolutional neural network (CNN) extracts the features of the image through deeper layers, and has achieved impressive results. In this paper, we propose a single image super-resolution model based on Adaptive Deep Residual named as ADR-SR, which uses the Input Output Same Size (IOSS) structure, and releases the dependence of upsampling layers compared with the existing SR methods. Specifically, the key element of our model is the Adaptive Residual Block (ARB), which replaces the commonly used constant factor with an adaptive residual factor. The experiments prove the effectiveness of our ADR-SR model, which can not only reconstruct images with better visual effects, but also get better objective performances.
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Acknowledgements
This work was supported in part by National Natural Science Foundation of China (No. 61571046) and National Key R&D Program of China (No. 2017YFF0209806).
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Shuai Liu received his B.S. degree from North China University of Technology, Beijing, China, in 2018. He is now a postgraduate student in North China University of Technology. His current interests include deep learning and image processing. He win the ICCV 2019 AIM Challenge on Constrained Super-Resolution Runner-up Award.
Ruipeng Gang received her B.S. degree from North China University of Technology, Beijing, China, in 2017. She is now a postgraduate student in North China University of Technology. Her current interests include deep learning and image processing. She win the ICCV 2019 AIM Challenge on Constrained Super-Resolution Runner-up Award.
Chenghua Li is a research asisitant in Institute of Automation, Chinese Academy of Sciences (CASIA). He received his Ph.D. degree in National Laboratory of Pattern Recognition from CASIA, Beijing, in 2018. He received his B.S. degree from North China University of Technology, Beijing, China in 2014, and his M.S. degree from Anyang Normal University, Henan Province, China in 2011. His research interests include deep learning and compute vision.
Ruixia Song graduated from Beijing Normal University in 1980, and received her master degree in science from Hangzhou University (now Zhejiang University) in 1990. She is now a professor in North China University of Technology. Her major areas of research interest include computer graphics, image processing, and pattern recognition, etc.
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Liu, S., Gang, R., Li, C. et al. Adaptive deep residual network for single image super-resolution. Comp. Visual Media 5, 391–401 (2019). https://doi.org/10.1007/s41095-019-0158-8
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DOI: https://doi.org/10.1007/s41095-019-0158-8