基于加速双三次插值的SRCNN数字岩心图像超分辨率

Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering Research Pub Date : 2020-06-19 DOI:10.1145/3411016.3411162

Yunfeng Bai, V. Berezovsky, V. Popov

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引用次数: 1

摘要

超分辨率卷积神经网络(SRCNN)已被证明具有提高图像分辨率的能力。应用SRCNN对岩心数字图像进行了增强，对岩心分析具有重要意义。在这个过程中，我们注意到作为SRCNN第一步的双三次插值算法可能会通过调整计算策略来提高速度。提出了一种基于加速双三次插值的SRCNN算法，并用2000张数字岩心图像进行了性能测试。实验表明，加速双三次插值算法比改进的基于区域的双三次图像插值算法和标准双三次插值算法更快，并证明了基于本文算法的SRCNN生成更高分辨率数字岩心图像的可行性。

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Digital Rock Core Images Super Resolution via SRCNN Based on Accelerated Bicubic Interpolation

The capability of Super Resolution Convolutional Neural Networks (SRCNN) has been proved to enhance resolution of images. We applied SRCNN to enhance digital rock core images that play an important role in analyzing rock core. In this process, we noticed that the bicubic interpolation algorithm that is the first step of SRCNN might improve the speed by adjusting the calculation strategy. We proposed an SRCNN based on accelerated bicubic interpolation and tested the performance with 2000 digital rock core images. The experiment demonstrated that the accelerated bicubic interpolation algorithm faster than improved region-based bicubic image interpolation algorithm and standard bicubic interpolation algorithm, and demonstrated the feasibility of SRCNN based on our proposed algorithm to produce higher resolution digital rock core images.

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Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering Research

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