[1]朱联祥*,仝文东,牛文煜,等.多尺度特征融合 ESRGAN 的岩石显微图像超分辨研究[J].计算机技术与发展,2023,33(07):55-60.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 008]
　ZHU Lian-xiang*,TONG Wen-dong,NIU Wen-yu,et al.Research on Rock Micro-image Super-resolution Based on Multiscale-fusion Feature ESRGAN[J].,2023,33(07):55-60.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 008]
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多尺度特征融合 ESRGAN 的岩石显微图像超分辨研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年07期
页码:
55-60
栏目:
媒体计算
出版日期:
2023-07-10

文章信息/Info

Title:
Research on Rock Micro-image Super-resolution Based on Multiscale-fusion Feature ESRGAN
文章编号:
1673-629X(2023)07-0055-06
作者:
朱联祥* ; 仝文东; 牛文煜; 邵浩杰
西安石油大学 计算机学院,陕西 西安 710065
Author(s):
ZHU Lian-xiang* ; TONG Wen-dong; NIU Wen-yu; SHAO Hao-jie
School of Computer Science,Xi’ an Shiyou University,Xi’ an 710065,China
关键词:
岩石显微图像; 深度学习; 超分辨率重建; 生成对抗网络; 密集卷积网络; 多尺度特征融合
Keywords:
rock micro - image;  deep learning;  super - resolution reconstruction;  generative adversarial network;  dense convolutional networks; multiscale feature fusion
分类号:
TP317. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 07. 008
摘要:
岩石显微图像可以反映油气藏的分布情况,对石油勘探等行业具有很高的应用价值。 针对岩石显微图像在超分辨处理时存
在岩石特性模糊、分辨率低、丢失细节信息等问题,基于 ESRGAN 和多尺度特征融合,对网络结构进行优化,在ESRGAN 的 RRDB 块中加入多尺度特征融合方法,提出一种岩石显微图像超分辨率重建算法。 采用 DRSRD1_2D 岩石显微图像数据集进行 4 倍超分辨重建实验,通过峰值信噪比( PSNR) 、结构相似性(SSIM)及感知系数(PI) 对重建结果进行评价,并将所提算法与 SRGAN、SFT-GAN、ESRGAN 方法进行对比。 结果表明:在碳酸岩数据集上,该算法的三项指标在几种算法中均为最优;在砂岩数据集上,该算法的 PSNR 和 PI 指标最优,SSIM 则为次优。 此外,该算法在视觉效果上也有着良好表现,能更好地表达图像的细节特征。
Abstract:
Rock microscopic images can reflect the distribution of oil and gas reservoirs,and has high application value for petroleum exploration and other industries. In order to solve the problems of fuzzy rock characteristics,low resolution
and loss of detail information,we propose a rock micro images super-resolution reconstruction algorithm based on ESRGAN and multi-scale feature fusion,with theadding of multi-scale feature fusion method to the RRDB block of ESRGAN. DRSRD1_2D rock microscopic image data set is used forthe experimental test of proposed method in 4x super - resolution and its performance is compared to that of SRGAN,SFT - GAN andESRGAN regarding to Peak?
Signal to Noise Ratio ( PSNR) ,Structural Similarity ( SSIM) and Perceptual Index ( PI) . The results showthat all three performance indexes of proposed method are the best among them on carbonatite data set,and PSNR and PI indexes ofwhich are the best,and SSIM index second best on sandstone data set. Meanwhile,the proposed method has a good visual effect of re-constructed image where better details can be manifested.

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更新日期/Last Update: 2023-07-10