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.

本文介绍了一种基于 U-Net 架构的多尺度提升去雾网络，包含了稠密特征融合、 Strengthen-Operate-Subtract 策略等内容，实验表明该方法在公开数据集和实际雾霾图像上表现优异。