  This paper proposes an end-to-end Efficient Re-parameterizationResidual
Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image.
The contribution of this paper mainly has the following three aspects: 1) A
novel Multi-branch Attention (MA) block. The spatial attention mechanism better
reconstructs high-frequency features, and the channel attention mechanism
treats the features of different channels differently. Multi-branch structure
dramatically improves the representation ability of the model and can be
changed into a single path structure after re-parameterization to speed up the
process of inference. Local Residual Connection allows the low-frequency
information in the nonhomogeneous area to pass through the block without
processing so that the block can focus on detailed features. 2) A lightweight
network structure. We use cascaded MA blocks to extract high-frequency features
step by step, and the Multi-layer attention fusion tail combines the shallow
and deep features of the model to get the residual of the clean image finally.
3)We propose two novel loss functions to help reconstruct the hazy image
ColorAttenuation loss and Laplace Pyramid loss. ERRA-Net has an impressive
speed, processing 1200x1600 HD quality images with an average runtime of 166.11
fps. Extensive evaluations demonstrate that ERSANet performs favorably against
the SOTA approaches on the real-world hazy images.
