Abstract
Convolutional neural networks (CNN) have been successfully applied in many fields of image processing, such as deblurring, denoising and image restoration. Estimating a high quality high-resolution image from one or a set of low-resolution images is a non-linear mapping, which can be formulated as a regression problem. According to the image formation process, a Deep Convolutional Network-based image Super-Resolution model DCNSR is proposed and is trained using end-to-end. Several key components of DCNSR, which would affect the training time and the effectiveness of reconstruction super-resolution image, are firstly demonstrated. Then, the deblurring performance is evaluated. Finally, comparisons with the results in state-of-the-arts are presented. Experimental results demonstrate that the proposed model achieves a notable improvement in terms of both quantitative and qualitative measurements.
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Acknowledgments
The authors would like to thank supports from the National Natural Science Foundation of China under Grant 51277091 and Grant 61179011, the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT_15R10), the Natural Science Foundation of Fujian Province of China under Grant 2011J01219 and the Educational Research Projects for Young and Middle-aged Teachers in Fujian Province under Grant JA15433.
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Lin, G., Wu, Q., Huang, X., Qiu, L., Chen, X. (2017). Deep Convolutional Networks-Based Image Super-Resolution. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_31
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DOI: https://doi.org/10.1007/978-3-319-63309-1_31
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