Images captured in hazy outdoor conditions often suffer from colour distortion, low contrast, and loss of detail, which impair high-level vision tasks. Single image dehazing is essential for applications such as autonomous driving and surveillance, with the aim of restoring image clarity. In this work, we propose WTCL-Dehaze an enhanced semi-supervised dehazing network that integrates Contrastive Loss and Discrete Wavelet Transform (DWT). We incorporate contrastive regularization to enhance feature representation by contrasting hazy and clear image pairs. Additionally, we utilize DWT for multi-scale feature extraction, effectively capturing high-frequency details and global structures. Our approach leverages both labelled and unlabelled data to mitigate the domain gap and improve generalization. The model is trained on a combination of synthetic and real-world datasets, ensuring robust performance across different scenarios. Extensive experiments demonstrate that our proposed algorithm achieves superior performance and improved robustness compared to state-of-the-art single image dehazing methods on both benchmark datasets and real-world images.

该研究解决了在雾霾户外条件下捕获图像时出现的颜色失真、对比度低和细节丢失的问题。提出的WTCL-Dehaze网络结合了对比损失和离散小波变换，利用对比正则化增强特征表示，并通过多尺度特征提取捕捉高频细节。实验结果表明，该方法在去雾效果和鲁棒性方面优于当前最先进的单图像去雾技术。

WTCL-去雾：通过小波变换和对比学习重新思考现实图像去雾