A two-stage image enhancement network for complex underground coal mine environment

Video surveillance images in coal mines often suffer from overall darkness, low contrast, and strong background noise. Developing comprehensive algorithms that can effectively enhance low-light images while simultaneously reducing noise remains a challenging task. This paper presents a two-stage image enhancement algorithm tailored for mine environments, employing deep learning techniques. The algorithm comprises a low-light enhancement stage that utilizes light enhancement curves to improve image sharpness and contrast, followed by a denoising stage that removes noise from the enhanced images while preserving crucial mine image details. Furthermore, two dedicated mine image datasets were constructed to evaluate the proposed method. Experimental results on these mine image datasets and the BSD300 dataset demonstrate that the algorithm can significantly improve performance, achieving state-of-the-art results by synergistically combining brightness enhancement and denoising tailored for low-light mine conditions.