中图分类号： TP391.41；TP183
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2020.10.001
引用格式： 黄帅坤，陈洪刚，卿粼波，等. Raw格式岩心图像超分辨率重建[J].信息技术与网络安全，2020，39(10)：1-6.

Super-resolution reconstruction of Raw core image

Abstract： In the development of core array camera, the learning based super resolution technology can be used to improve the resolution of core image. In order to solve the problems of detail blur or color deviation in the reconstruction of core images by existing super-resolution technologies, this paper proposes a Raw core image super-resolution reconstruction algorithm based on deep convolutional neural network. Firstly, the linear processing part of the analog camera image processor synthesizes the linear image data set. Then, a two-layer convolutional neural network is used to train the texture and color mapping relationship between high and low resolution images. Finally, the reconstructed linear high-resolution image simulates the nonlinear processing part of the camera image processor to obtain the core reconstruction image with clear texture and realistic color. Experimental results show that the reconstruction algorithm proposed in this paper improves the reconstruction effect of core images.

Key words : Raw image；linear image；surper resolution reconstruction；convolutional neural network