Traffic Image Defogging Method under Hazy Environments Based on Frequency Domain Fusion and Attention Mechanism

Clear visual data is a fundamental prerequisite for real-time detection of and decision-making about abnormal traffic conditions in highway intelligent traffic systems (ITS). However, as a frequent weather phenomenon, haze can significantly reduce image contrast and obscure critical visual features, posing a serious threat to the effectiveness of highway ITS. This paper proposes an image defogging method based on spatial-frequency domain context joint convolutional network, aiming to address the limitations of existing methods in capturing frequency domain correlations and reducing spatial redundancy. Firstly, the method decomposes the haze characteristics from the spatial-frequency domain perspective, establishes a frequency domain defogging model more suitable for hazy scenes, and fully utilizes multi-scale frequency features to represent the correlation between the fog layer and the clear background. Secondly, based on the scale-space transformation and frequency-division feature mapping strategy, the feature correlations of low-frequency and high-frequency components are effectively decomposed, and redundancy is eliminated to improve computational efficiency. Then, a multi-layer channel attention mechanism is introduced, enabling the model to flexibly extract the weight information of haze distributions at different layers based on the frequency-domain feature decomposition, strengthening the attention to key features, and gradually stripping the complex overlapping fog layers through a sequential iterative approach. Finally, a defogging dataset is constructed to verify the effectiveness of the proposed method. Experiment results demonstrate that the proposed method achieves a peak signal-to-noise ratio of 30.3 dB and a structural similarity index measure of 0.9, outperforming existing methods. These results confirm the effectiveness of our method in restoring image clarity and preserving structural details, offering practical support for highway ITS under adverse weather conditions.