Images captured in hazy and smoky environments suffer from reduced visibility, posing a challenge when monitoring infrastructures and hindering emergency services during critical situations. The proposed work investigates the use of the deep learning models to enhance the automatic, machine-based readability of gauge in smoky environments, with accurate gauge data interpretation serving as a valuable tool for first responders. The study utilizes two deep learning architectures, FFA-Net and AECR-Net, to improve the visibility of gauge images, corrupted with light up to dense haze and smoke. Since benchmark datasets of analog gauge images are unavailable, a new synthetic dataset, containing over 14,000 images, was generated using the Unreal Engine. The models were trained with an 80\% train, 10\% validation, and 10\% test split for the haze and smoke dataset, respectively. For the synthetic haze dataset, the SSIM and PSNR metrics are about 0.98 and 43\,dB, respectively, comparing well to state-of-the art results. Additionally, more robust results are retrieved from the AECR-Net, when compared to the FFA-Net. Although the results from the synthetic smoke dataset are poorer, the trained models achieve interesting results. In general, imaging in the presence of smoke are more difficult to enhance given the inhomogeneity and high density. Secondly, FFA-Net and AECR-Net are implemented to dehaze and not to desmoke images. This work shows that use of deep learning architectures can improve the quality of analog gauge images captured in smoke and haze scenes immensely. Finally, the enhanced output images can be successfully post-processed for automatic autonomous reading of gauges

翻译：在薄雾与烟雾环境中捕获的图像存在能见度降低的问题，这对基础设施监控构成挑战，并在危急情况下阻碍应急服务。本研究探讨了利用深度学习模型增强烟雾环境中仪表自动、基于机器的可读性，准确的仪表数据解读可作为急救人员的宝贵工具。该研究采用两种深度学习架构——FFA-Net与AECR-Net——来改善受轻度至重度薄雾及烟雾干扰的仪表图像可见度。由于缺乏模拟仪表图像的基准数据集，本研究使用虚幻引擎生成了一个包含超过14,000张图像的新合成数据集。模型训练分别针对薄雾和烟雾数据集采用80%训练、10%验证和10%测试的划分。对于合成薄雾数据集，SSIM与PSNR指标分别约为0.98和43 dB，与现有先进成果相比表现良好。此外，与FFA-Net相比，AECR-Net取得了更稳健的结果。尽管合成烟雾数据集的结果较差，但训练后的模型仍获得了有意义的结果。总体而言，由于烟雾的不均匀性和高密度特性，其存在下的图像增强更为困难。其次，FFA-Net与AECR-Net本是为图像去雾而非去烟设计。本研究表明，深度学习架构的使用能极大提升烟雾与薄雾场景下捕获的模拟仪表图像质量。最后，增强后的输出图像可成功进行后处理，以实现仪表的自动自主读数。