Abstract
Higher performance is the eternal purpose for super-resolution (SR) methods to pursue. Since the deep convolution neural network is introduced into this issue successfully, many SR methods have achieved impressive results. To further improve the accuracy that current SR methods have achieved, we propose a high-accuracy deep convolution network (HDCN). In this article, deeper network structure is deployed for reconstructing images with a fixed upscaling factor and the magnification becomes alternative by cascading HDCN. \(L_2\) loss function is substituted by a more robust one for reducing the blurry prediction. In addition, gradual learning is adopted for accelerating the rate of convergence and compacting the training process. Extensive experiment results prove the effectiveness of these ingenious strategies and demonstrate the higher-accuracy of proposed model among state-of-the-art SR methods.
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Acknowledgment
The paper is supported in part by the Natural Science Foundation of China (No. 61672022 and No. 61272036), the Key Discipline Foundation of Shanghai Second Polytechnic University (No. XXKZD1604), and the Graduate Innovation Program (No. A01GY17F022).
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Tan, W., Guo, X. (2017). High-Accuracy Deep Convolution Neural Network for Image Super-Resolution. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_23
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DOI: https://doi.org/10.1007/978-3-319-68935-7_23
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