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Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs.

Liang Zou¹, Shifan Xu¹, Weiming Zhu¹

¹School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This study introduces a novel Generative Adversarial Network (GAN) to enhance low-resolution coal photomicrographs. The advanced GAN produces clearer, more realistic images, improving coal analysis and quality assessment.

Keywords:

coal photomicrographs restoration generative adversarial net super-resolution wide residual block

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Area of Science:

Geology
Materials Science
Computer Vision

Background:

Maceral analysis of coal photomicrographs is crucial for understanding coal properties and quality.
Low-resolution images due to equipment limitations hinder detailed analysis.
Existing image restoration methods often lack clarity and realism.

Purpose of the Study:

To develop a novel Generative Adversarial Network (GAN) for restoring high-definition coal photomicrographs.
To improve the clarity and realism of coal images for better maceral analysis.
To overcome the limitations of traditional image restoration techniques.

Main Methods:

Implementation of a lightweight GAN-based network for image super-resolution.
Integration of Wide Residual Blocks to enhance artifact elimination and non-linear fitting.
Inclusion of a multi-scale attention block in the generator for capturing long-range feature correlations.

Main Results:

The proposed GAN method achieved a peak signal-to-noise ratio (PSNR) of 31.12 dB.
The method obtained a structural similarity index (SSIM) of 0.906.
These results significantly surpass state-of-the-art super-resolution reconstruction approaches.

Conclusions:

The novel GAN effectively restores high-definition coal photomicrographs, yielding explicit and realistic results.
The enhanced image quality facilitates more accurate coal characteristic and quality assessment.
This approach represents a significant advancement over traditional methods for coal image super-resolution.