LKD-Net: Large Kernel Convolution Network for Single Image Dehazing

The previous deep CNN-based single-image dehazing methods are devoted to improving the performance by increasing the network’s depth and width. In this paper, a novel Large Kernel Convolution Dehaze Network (LKD-Net) is proposed to enhance the network’s performance by increasing the size of the convolutional kernel. The main module in LKD-Net is the designed Large Kernel Convolution Dehaze Block (LKD Block), which consists of the Decomposition deep-wise Large Kernel Convolution Block (DLKCB) and the Channel Enhanced Feed-forward Network (CEFN). DLKCB is designed to reduce the massive amount of computational overhead and parameters of the large kernel by splitting the deep-wise large kernel convolution into a smaller depth-wise convolution and a depth-wise dilated convolution. Meanwhile, CEFN is designed to enhance the robustness of the Feed-forward Network by exploiting significant channels. The extensive experiments on RESIDE dataset demonstrate that our LKD-Net outperforms the state-of-the-art with far less computational overhead and parameters.