LAMNet: a lightweight attention mechanism dehazing network
qian xiong · 2024
Image dehazing is an important task in computer vision, as it can transform unclear images taken in haze weather into clear ones. Some deep-learning based dehazing models struggle to balance the relationship between the convolutional kernel receptive field and the number of parameters when improving model performance. We propose dilated convolutional kernels with a size of 13 to balance between the receptive field and the number of parameters. Furthermore, some deeplearning-based dehazing methods employ a significant number of channel attention mechanisms and spatial attention mechanisms, both of which can share a common fully connected layer. Our proposed fused attention mechanism incorporates the effects of both channel attention and spatial attention mechanisms, while reducing the number of parameters. Combining these two modules, we introduce a lightweight attention mechanism dehazing network (LAMNet). This network demonstrates effective dehazing results while maintaining a relatively low number of parameters.