Remove haze in an image with detail-enhanced convolution and content-guided attention with DEA-Net

Remove haze in an image with detail-enhanced convolution and content-guided attention with DEA-Net

DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention

arXiv paper abstract https://arxiv.org/abs/2301.04805

arXiv PDF paper https://arxiv.org/pdf/2301.04805.pdf

GitHub https://github.com/cecret3350/DEA-Net

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images.

... 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.

... By combining above ... detail-enhanced attention network (DEA-Net) for recovering high-quality haze-free images ... outperforming the state-of-the-art (SOTA) methods ...

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