{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:55:57Z","timestamp":1776131757466,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["2019ZDPY17"],"award-info":[{"award-number":["2019ZDPY17"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2021DJ0107"],"award-info":[{"award-number":["2021DJ0107"]}]},{"name":"Scientific Research and Technological Development Project of CNPC","award":["2019ZDPY17"],"award-info":[{"award-number":["2019ZDPY17"]}]},{"name":"Scientific Research and Technological Development Project of CNPC","award":["2021DJ0107"],"award-info":[{"award-number":["2021DJ0107"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Analyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal\u2019s 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.<\/jats:p>","DOI":"10.3390\/s23167296","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T09:07:16Z","timestamp":1692608836000},"page":"7296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7322-5735","authenticated-orcid":false,"given":"Liang","family":"Zou","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Shifan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Weiming","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Xiu","family":"Huang","sequence":"additional","affiliation":[{"name":"Research Institute of Petroleum Exploration and Development, Beijing 100083, China"}]},{"given":"Zihui","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Kun","family":"He","sequence":"additional","affiliation":[{"name":"Research Institute of Petroleum Exploration and Development, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120475","DOI":"10.1016\/j.fuel.2021.120475","article-title":"Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved U-net","volume":"294","author":"Lei","year":"2021","journal-title":"Fuel"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s40789-020-00321-4","article-title":"Clean coal geology in China: Research advance and its future","volume":"7","author":"Wang","year":"2020","journal-title":"Int. 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