Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs
Analyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal’s characteristics, quality, and potential uses. However, due to limitations of equipment and technology, the obtained coal photomicrographs may have low resolution, failing to show cl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/16/7296 |
| _version_ | 1797583217543348224 |
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| author | Liang Zou Shifan Xu Weiming Zhu Xiu Huang Zihui Lei Kun He |
| author_facet | Liang Zou Shifan Xu Weiming Zhu Xiu Huang Zihui Lei Kun He |
| author_sort | Liang Zou |
| collection | DOAJ |
| description | Analyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal’s characteristics, quality, and potential uses. However, due to limitations of equipment and technology, the obtained coal photomicrographs may have low resolution, failing to show clear details. In this study, we introduce a novel Generative Adversarial Network (GAN) to restore high-definition coal photomicrographs. Compared to traditional image restoration methods, the lightweight GAN-based network generates more explicit and realistic results. In particular, we employ the Wide Residual Block to eliminate the influence of artifacts and improve non-linear fitting ability. Moreover, we adopt a multi-scale attention block embedded in the generator network to capture long-range feature correlations across multiple scales. Experimental results on 468 photomicrographs demonstrate that the proposed method achieves a peak signal-to-noise ratio of 31.12 dB and a structural similarity index of 0.906, significantly higher than state-of-the-art super-resolution reconstruction approaches. |
| first_indexed | 2024-03-10T23:35:44Z |
| format | Article |
| id | doaj.art-83860443ae424581aec1989fade2b540 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:35:44Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-83860443ae424581aec1989fade2b5402023-11-19T02:59:34ZengMDPI AGSensors1424-82202023-08-012316729610.3390/s23167296Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal PhotomicrographsLiang Zou0Shifan Xu1Weiming Zhu2Xiu Huang3Zihui Lei4Kun He5School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaResearch Institute of Petroleum Exploration and Development, Beijing 100083, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaResearch Institute of Petroleum Exploration and Development, Beijing 100083, ChinaAnalyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal’s characteristics, quality, and potential uses. However, due to limitations of equipment and technology, the obtained coal photomicrographs may have low resolution, failing to show clear details. In this study, we introduce a novel Generative Adversarial Network (GAN) to restore high-definition coal photomicrographs. Compared to traditional image restoration methods, the lightweight GAN-based network generates more explicit and realistic results. In particular, we employ the Wide Residual Block to eliminate the influence of artifacts and improve non-linear fitting ability. Moreover, we adopt a multi-scale attention block embedded in the generator network to capture long-range feature correlations across multiple scales. Experimental results on 468 photomicrographs demonstrate that the proposed method achieves a peak signal-to-noise ratio of 31.12 dB and a structural similarity index of 0.906, significantly higher than state-of-the-art super-resolution reconstruction approaches.https://www.mdpi.com/1424-8220/23/16/7296coal photomicrographs restorationsuper-resolutiongenerative adversarial netwide residual block |
| spellingShingle | Liang Zou Shifan Xu Weiming Zhu Xiu Huang Zihui Lei Kun He Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs Sensors coal photomicrographs restoration super-resolution generative adversarial net wide residual block |
| title | Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs |
| title_full | Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs |
| title_fullStr | Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs |
| title_full_unstemmed | Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs |
| title_short | Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs |
| title_sort | improved generative adversarial network for super resolution reconstruction of coal photomicrographs |
| topic | coal photomicrographs restoration super-resolution generative adversarial net wide residual block |
| url | https://www.mdpi.com/1424-8220/23/16/7296 |
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