Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

• URL: http://arxiv.org/abs/2004.13388v1
• Date: Tue, 28 Apr 2020 09:34:47 GMT
• Title: Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
• Authors: Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, Ming-Hsuan Yang
• Abstract summary: We propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. We show that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.
• Score: 92.92572594942071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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