Single image haze removal network based on detail-enhanced convolution and channel-spatial attention

Currently, traditional deep learning-based single-image dehazing networks often rely on conventional convolution operations, accompanied by issues such as large model parameters and slow processing speed. To address these problems, we designed a lightweight image dehazing network model by introducing a new convolution method combined with channel and spatial attention mechanisms. The model improves the dehazing effect by enhancing the ability to extract features and strengthening feature details. Experimental results show that the model designed in this paper improves the details of important features, enhances the brightness of the dehazed image, prevents distortion, and achieves good dehazing performance.