Year: 2025
Type: article
Abstract: The graphite mineral processing workshop involves complex procedures and generates a large amount of dust and smoke during operation. This particulate matter significantly degrades the quality of indoor surveillance video frames, thereby affecting subsequent tasks such as image segmentation and recognition. Existing image dehazing algorithms often suffer from insufficient feature extraction or excessive computational cost, which limits their real-time applicability and makes them unsuitable for deployment in graphite processing environments. To address this issue, this paper proposes a CNN-based dehazing algorithm tailored for dust and haze removal in graphite mineral processing workshops. Experimental results on a synthetic haze dataset constructed for graphite processing scenarios demonstrate that the proposed method achieves higher PSNR and SSIM compared to existing deep learning-based dehazing approaches, resulting in improved visual quality of dehazed images.
Source: Applied Sciences
DOI: 10.3390/app15179320
Authors: Sifan Li, Xueyu Huang, Zeyang Qiu
Institution: Jiangxi University of Science and Technology
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G1715350728
OpenAlex ID: W4413619406