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Keywords = coal photomicrographs restoration

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16 pages, 1319 KB  
Article
Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs
by Liang Zou, Shifan Xu, Weiming Zhu, Xiu Huang, Zihui Lei and Kun He
Sensors 2023, 23(16), 7296; https://doi.org/10.3390/s23167296 - 21 Aug 2023
Cited by 10 | Viewed by 3435
Abstract
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 [...] Read more.
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. Full article
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