In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.

中文翻译：

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基于深度差和照度调整的图像增强算法

为提高雾霾和光照不均复杂环境下交通图像的清晰度和色彩保真度，促进道路交通安全监测，提出了一种基于光照调整和景深差异的交通图像增强模型。该算法基于Retinex理论，利用暗通道原理获取图像景深，利用光谱聚类算法对图像深度进行聚类。子图像分割后，根据景深估计局部雾度浓度，对子图像进行自适应增强和融合。此外，通过多尺度引导滤波得到光照分量，保持图像的边缘特征，通过调整曲线函数解决光照不均问题。实验结果表明，所提模型能够有效增强交通场景中光照不均和雾霾天气图像，图像视觉效果良好。生成的图像细节丰富，提高了交通图像的质量，能够满足交通实际应用的需要。