A hybrid zero-reference and dehazing network for joint low-light underground image enhancement

The raw images captured by underground vision sensors in underground mine settings are disturbed by dim lighting, high dust levels, and complex electromagnetic conditions, suffering from high noise, low illumination, and low-resolution contamination, which further affects the supervision of the vision sensors. However, existing image enhancement methods relying on synthesized datasets are not suitable for improving images in real underground mine settings. This study focuses on addressing these challenges. We collected a large number of underground mine images and proposed a novel image enhancement approach. Inspired by visual image processing techniques, this approach combines low-light enhancement and dehazing methods to address the issues of uneven lighting and fog distortion. Specifically, the proposed Zero-Reference Depth Curve Estimation-Dehazing Network (Z-DCE-DNet) aims to enhance underground mine images. It addresses two key aspects: (1) enhancing low-light images by incorporating higher-order loss curves into the DCE-Net backbone and introducing a new loss function to optimize network learning for improved low-light image quality; (2) addressing the color distortion and blur caused by low light enhancement through post-processing using convolutional neural networks, with AOD-Net enhancing the clarity of downhole images. Extensive experimental results demonstrate that the Z-DCE-DNet method produces visually superior enhanced images, and comparative analyses of multiple object detectors reveal that the enhanced images lead to improved detection outcomes.