@ARTICLE{Luo_Hui_Improved_2024,
 author={Luo, Hui and Zeng, Qingwen and Luo, Xiaoyan and Hu, Zhen},
 volume={vol. 40},
 number={No 4},
 pages={131–146},
 journal={Gospodarka Surowcami Mineralnymi - Mineral Resources Management},
 howpublished={online},
 year={2024},
 publisher={Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN},
 publisher={Instytut Gospodarki Surowcami Mineralnymi i Energią PAN},
 abstract={At the ore crushing site, the crushed ore must be washed to remove sediment, and the washing step puts the detected ore in a watery environment, resulting in the presence of reflective areas in the image of watery ore particles. Aiming at the problem of mis-segmentation of ore images due to the masking of ore feature information by the reflective area, an improved Pix2PixGAN model is proposed to solve the problem of removing water and repairing the reflective area in watery ore images. The ResNet network with good stability is used to comprehensively extract the features of watery ore images, improve the stability of network training, introduce the structural similarity loss function, and update the network parameters by minimising the structural similarity loss value to reduce the structural differences between the reconstructed image and the real image. The experimental results show that the improved Pix2PixGAN model compares with the Pix2PixGAN and CycleGAN models; the watery ore image removes the water image reflection restoration better and, at the same time, improves the structural edge clarity of the generated dry ore particle image. The PSNR and SSIM evaluation metrics are improved by 8.8 and 1.28%, respectively, further verifying the effectiveness of the improved algorithm. This innovative approach provides a feasible solution for image processing at the ore-crushing site. It is of great significance for the subsequent enhancement of image recognition, segmentation, and reduction of misjudgment.},
 title={Improved Pix2PixGAN water-bearing ore reflection image restoration method},
 type={Article},
 URL={http://czasopisma.pan.pl/Content/133718/Zeng%20i%20inni.pdf},
 doi={10.24425/gsm.2024.152724},
 keywords={loss function, ore particle, water removal, generative adversarial network},
}