The working environment underground in coal mines is harsh,and the monitoring images are blurred due to complex lighting and dust mist,which greatly affects the safety of coal mining operations.The existing methods for synthesizing dust and mist datasets as-sume that the atmospheric light is uniform,which does not hold in coal mining scenarios,resulting in poor generalization performance of deep defogging algorithms in coal mining scenarios.In response to this issue,the depth information of clear images were first estimated,and used Retinex theory to obtain the spatial brightness information of fogged images.Combining the depth information and brightness information of the images,a neural network was trained to generate hazy images that can reflect the spatial brightness changes under loss constraints such as brightness consistency.In addition,considering the complex lighting in the real scene of coal mines,clear images were further enhanced to the dim artifacts after image dehazing.Experimental results demonstrate that the proposed method is suitable for synthesizing the realistic haze/clear dataset in coal mine.
关键词
光照/尘雾图像/Retinex/图像增强/深度信息
Key words
illumination/hazy images/Retinex/image enhancement/depth information
维普
万方数据