论文标题
具有密度特征融合的多尺度增强飞行网络
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
论文作者
论文摘要
在本文中，我们提出了一个基于U-NET体系结构的密集特征融合的多尺度增强的飞行网络。提出的方法是基于两个原理设计的，即提升和错误反馈，我们表明它们适合除去危险问题。通过将增强 - 提取的提升策略纳入所提出的模型的解码器中，我们开发了一个简单而有效的增强解码器，以逐步恢复无雾图图像。为了解决U-NET体系结构中保存空间信息的问题，我们使用反向预测反馈方案设计了一个密集的功能融合模块。我们表明，密集的功能融合模块可以同时从高分辨率功能中纠正缺失的空间信息，并利用非粘合功能。广泛的评估表明，所提出的模型对基准数据集以及现实世界中的朦胧图像的最先进方法表现出色。
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.
