  The deep convolutional neural networks (CNNs)-based single image dehazing
methods have achieved significant success. The previous methods are devoted to
improving the network's performance by increasing the network's depth and
width. The current methods focus on increasing the convolutional kernel size to
enhance its performance by benefiting from the larger receptive field. However,
directly increasing the size of the convolutional kernel introduces a massive
amount of computational overhead and parameters. Thus, a novel Large Kernel
Convolution Dehaze Block (LKD Block) consisting of the Decomposition deep-wise
Large Kernel Convolution Block (DLKCB) and the Channel Enhanced Feed-forward
Network (CEFN) is devised in this paper. The designed DLKCB can split the
deep-wise large kernel convolution into a smaller depth-wise convolution and a
depth-wise dilated convolution without introducing massive parameters and
computational overhead. Meanwhile, the designed CEFN incorporates a channel
attention mechanism into Feed-forward Network to exploit significant channels
and enhance robustness. By combining multiple LKD Blocks and Up-Down sampling
modules, the Large Kernel Convolution Dehaze Network (LKD-Net) is conducted.
The evaluation results demonstrate the effectiveness of the designed DLKCB and
CEFN, and our LKD-Net outperforms the state-of-the-art. On the SOTS indoor
dataset, our LKD-Net dramatically outperforms the Transformer-based method
Dehamer with only 1.79% #Param and 48.9% FLOPs. The source code of our LKD-Net
is available at https://github.com/SWU-CS-MediaLab/LKD-Net.
