UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement
Wenwu Han,
Yigai Xiao,
Yu Yin
Abstract:In recent years, low‐light image enhancement has become increasingly active. However, in underground mine environments, acquiring high‐quality images is still challenging due to low light, low contrast, and occlusion. To address this problem, this study proposes a low‐light image enhancement method for underground mine based on generative adversarial networks (UM‐GAN), which aims to take full advantage of the ability of GAN to achieve the restoration of details, the reduction of noise, and the improvement of o… Show more
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