Traffic sign dehazing based on improved conditional generative adversarial network

Aiming at the restoration of traffic sign images in foggy weather, this paper proposes a traffic sign defogging algorithm based on an improved Conditional Generative Adversarial Network (CGAN). Firstly, the algorithm introduces a generator with a symmetric skip connection structure and designs a novel loss function. Secondly, the TT100K high-definition image dataset is utilized to construct pairs of clear-day and simulated foggy-day images, and the atmospheric scattering model is adopted to simulate different levels of haze for training. Finally, through comparison with other mainstream defogging algorithms, the experimental results demonstrate that the improved CGAN achieves superior defogging effects on the test set, significantly improving the clarity and recognition rate of traffic sign.