Abstract
Clear monitoring images are crucial for the safe operation of coal mine Internet of Video Things (IoVT) systems. However, low illumination and uneven brightness in underground environments significantly degrade image quality, posing challenges for enhancement methods that often rely on difficult-to-obtain paired reference images. Additionally, there is a trade-off between enhancement performance and computational efficiency on edge devices within IoVT this http URL address these issues, we propose a multimodal image enhancement method tailored for coal mine IoVT, utilizing an ISP-CNN fusion architecture optimized for uneven illumination. This two-stage strategy combines global enhancement with detail optimization, effectively improving image quality, especially in poorly lit areas. A CLIP-based multimodal iterative optimization allows for unsupervised training of the enhancement algorithm. By integrating traditional image signal processing (ISP) with convolutional neural networks (CNN), our approach reduces computational complexity while maintaining high performance, making it suitable for real-time deployment on edge this http URL results demonstrate that our method effectively mitigates uneven brightness and enhances key image quality metrics, with PSNR improvements of 2.9%-4.9%, SSIM by 4.3%-11.4%, and VIF by 4.9%-17.8% compared to seven state-of-the-art algorithms. Simulated coal mine monitoring scenarios validate our method's ability to balance performance and computational demands, facilitating real-time enhancement and supporting safer mining operations.
Abstract (translated)
清晰的监控图像对于煤矿物联网（IoVT）系统的安全运行至关重要。然而，地下环境中光线不足和亮度不均会显著降低图像质量，这对通常依赖于难以获取的配对参考图像进行增强的方法提出了挑战。此外，在IoVT设备中还存在着性能提升与计算效率之间的权衡问题。为了解决这些问题，我们提出了一种专门针对煤矿IoVT的多模态图像增强方法，并采用了一个优化了不均匀照明条件下的ISP-CNN融合架构。这一两阶段策略结合了全局增强和细节优化，有效提升了图像质量，尤其是在光线不足的情况下。 基于CLIP（Contrastive Language–Image Pre-training）的多模态迭代优化使我们能够在没有监督训练数据的情况下对增强算法进行培训。通过将传统的图像信号处理技术（ISP）与卷积神经网络（CNN）相结合，我们的方法在保持高性能的同时减少了计算复杂性，使其适用于边缘设备上的实时部署。 实验结果表明，与七种最先进的算法相比，我们的方法能够有效缓解亮度不均，并提高了关键的图像质量指标：PSNR（峰值信噪比）提升了2.9%-4.9%，SSIM（结构相似度指数）提升了4.3%-11.4%，VIF（视觉信息量）提升了4.9%-17.8%。模拟煤矿监控场景验证了我们的方法能够在性能和计算需求之间找到平衡，从而支持实时增强并促进更安全的采矿作业。
URL
https://arxiv.org/abs/2502.19450
