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Green Mining, 2nd Volume
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School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Dr. Jianping Sun
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Green Mining, 2nd Volume

Abstract submission deadline
31 March 2025
Manuscript submission deadline
31 May 2025
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381

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic, “Green Mining”. Mining is the fundamental industry for social development and national economic construction. Throughout the entire process of exploring and developing mineral resources, scientific and orderly mining practices are implemented. Disturbance to the ecological environment in the mining area is kept under control within a manageable range. It is of great significance to recognize environmental ecology, employ scientific mining methods, efficiently utilize resources, digitize management information, and promote harmony within mining communities. This research topic aims to provide a platform for new research and recent advances in green mine technology. To promote the development of green mine construction, we encourage the submission of high-quality original research papers, including but not limited to the following topics:

  • Safety and sustainable mining;
  • Mineral resource management;
  • Intelligent mining technology;
  • Mining equipment;
  • Geomechanics and geophysics;
  • Rehabilitation of mine sites;
  • Human–machine–environment system;
  • Green exploration in mines;
  • Mine safety and personnel health;
  • Harmless treatment of solid waste in mines.

Prof. Dr. Kun Du
Dr. Jianping Sun
Topic Editors

Keywords

  • green technology
  • structure engineering
  • mining engineering
  • rock mechanics
  • environmental protection
  • life cycle of mines
  • fracture mechanics
  • slope stability
  • economics and policy

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Energies
energies 		3.2 5.5 2008 16.1 Days CHF 2600 Submit
Minerals
minerals 		2.5 3.9 2011 18.7 Days CHF 2400 Submit
Mining
mining 		- - 2021 15 Days CHF 1000 Submit
Processes
processes 		3.5 4.7 2013 13.7 Days CHF 2400 Submit
Sustainability
sustainability 		3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (1 paper)

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21 pages, 8918 KiB  
Article
Enhancement of Mine Images through Reflectance Estimation of V Channel Using Retinex Theory
by Changlin Wu, Dandan Wang, Kaifeng Huang and Long Wu
Processes 2024, 12(6), 1067; https://doi.org/10.3390/pr12061067 - 23 May 2024
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Abstract
The dim lighting and excessive dust in underground mines often result in uneven illumination, blurriness, and loss of detail in surveillance images, which hinders subsequent intelligent image recognition. To address the limitations of the existing image enhancement algorithms in terms of generalization and [...] Read more.
The dim lighting and excessive dust in underground mines often result in uneven illumination, blurriness, and loss of detail in surveillance images, which hinders subsequent intelligent image recognition. To address the limitations of the existing image enhancement algorithms in terms of generalization and accuracy, this paper proposes an unsupervised method for enhancing mine images in the hue–saturation–value (HSV) color space. Inspired by the HSV color space, the method first converts RGB images to the HSV space and integrates Retinex theory into the brightness (V channel). Additionally, a random perturbation technique is designed for the brightness. Within the same scene, a U-Net-based reflectance estimation network is constructed by enforcing consistency between the original reflectance and the perturbed reflectance, incorporating ResNeSt blocks and a multi-scale channel pixel attention module to improve accuracy. Finally, an enhanced image is obtained by recombining the original hue (H channel), brightness, and saturation (S channel), and converting back to the RGB space. Importantly, this image enhancement algorithm does not require any normally illuminated images during training. Extensive experiments demonstrated that the proposed method outperformed most existing unsupervised low-light image enhancement methods, qualitatively and quantitatively, achieving a competitive performance comparable to many supervised methods. Specifically, our method achieved the highest PSNR value of 22.18, indicating significant improvements compared to the other methods, and surpassing the second-best WCDM method by 10.3%. In terms of SSIM, our method also performed exceptionally well, achieving a value of 0.807, surpassing all other methods, and improving upon the second-place WCDM method by 19.5%. These results demonstrate that our proposed method significantly enhanced image quality and similarity, far exceeding the performance of the other algorithms. Full article
(This article belongs to the Topic Green Mining, 2nd Volume)
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