  Single image dehazing is a challenging ill-posed problem which estimates
latent haze-free images from observed hazy images. Some existing deep learning
based methods are devoted to improving the model performance via increasing the
depth or width of convolution. The learning ability of convolutional neural
network (CNN) structure is still under-explored. In this paper, a
detail-enhanced attention block (DEAB) consisting of the detail-enhanced
convolution (DEConv) and the content-guided attention (CGA) is proposed to
boost the feature learning for improving the dehazing performance.
Specifically, the DEConv integrates prior information into normal convolution
layer to enhance the representation and generalization capacity. Then by using
the re-parameterization technique, DEConv is equivalently converted into a
vanilla convolution with NO extra parameters and computational cost. By
assigning unique spatial importance map (SIM) to every channel, CGA can attend
more useful information encoded in features. In addition, a CGA-based mixup
fusion scheme is presented to effectively fuse the features and aid the
gradient flow. By combining above mentioned components, we propose our
detail-enhanced attention network (DEA-Net) for recovering high-quality
haze-free images. Extensive experimental results demonstrate the effectiveness
of our DEA-Net, outperforming the state-of-the-art (SOTA) methods by boosting
the PSNR index over 41 dB with only 3.653 M parameters. The source code of our
DEA-Net will be made available at https://github.com/cecret3350/DEA-Net.
