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arXiv · 2004.13388

Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

Hang Dong·Jinshan Pan·Lei Xiang·Zhe Hu·Xinyi Zhang·Fei Wang·Ming-Hsuan Yang
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Abstract

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles, boosting and error feedback, and we show that they are suitable for the dehazing problem. By incorporating the Strengthen-Operate-Subtract boosting strategy in the decoder of the proposed model, we develop a simple yet effective boosted decoder to progressively restore the haze-free image. To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme. We show that the dense feature fusion module can simultaneously remedy the missing spatial information from high-resolution features and exploit the non-adjacent features. Extensive evaluations demonstrate that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.

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Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, Ming-Hsuan Yang. 2020-04-28. Multi-Scale Boosted Dehazing Network with Dense Feature Fusion. https://arxiv.org/abs/2004.13388

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