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- Title
UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement.
- Authors
Han, Wenwu; Xiao, Yigai; Yin, Yu
- 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 overall image quality. The model proposed in this paper is divided into three main parts. Initially, a generator network adopting an encoder–decoder structure is developed. Subsequently, a novel strategy is introduced to merge information by utilizing inverted greyscale images and low‐light images as inputs. Further image quality enhancement is achieved by incorporating a noise reduction module employing the diffusion model. To ascertain the efficacy of the UM‐GAN method, evaluations are conducted on diverse real‐world and synthetic datasets, juxtaposing the approach against superior methods. Through qualitative and quantitative comparative experiments, the method showcases noteworthy advancements through qualitative and quantitative comparative experiments, substantiating its effectiveness. This research provides new ideas and methods for overcoming image quality problems in underground mine environments and contributes to the development of underground mine image processing.
- Subjects
MINES &; mineral resources; IMAGE intensifiers; GENERATIVE adversarial networks; IMAGE processing
- Publication
IET Image Processing (Wiley-Blackwell), 2024, Vol 18, Issue 8, p2154
- ISSN
1751-9659
- Publication type
Article
- DOI
10.1049/ipr2.13092