AFF-Dehazing: Attention-based feature fusion network for low-light image Dehazing,Computer Animation and Virtual Worlds

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AFF-Dehazing: Attention-based feature fusion network for low-light image Dehazing
Computer Animation and Virtual Worlds ( IF 0.9 ) Pub Date : 2021-05-24 , DOI: 10.1002/cav.2011
Yu Zhou ₁ , Zhihua Chen ₁ , Bin Sheng ₂ , Ping Li ₃ , Jinman Kim ₄ , Enhua Wu _{5,

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Affiliation

Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention-based feature fusion network (AFF-Dehazing) for low-light image dehazing. Our method decomposes the low-light image dehazing into two task-independent streams containing four modules: image dehazing module, low-light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention-based residual dense block. Since the dual-branch are used, AFF-Dehazing can avoid learning the mixed degradation all-in-one and enhance the details of low-light haze images. Extensive experiments show that our method surpasses previous state-of-the-art image dehazing methods and low-light enhancement methods by a very large margin both quantitatively and qualitatively.

中文翻译：

AFF-Dehazing：用于低光图像去雾的基于注意力的特征融合网络

在雾霾条件下拍摄的图像，尤其是在夜间光线不足的情况下，通常会出现能见度、对比度和鲜艳度降低的问题，这使得执行以下视觉任务变得困难。在本文中，我们提出了一种用于低光图像去雾的基于注意力的特征融合网络（AFF-Dehazing）。我们的方法将低光图像去雾分解为两个任务无关的流，包含四个模块：图像去雾模块、低光特征提取器模块、特征融合模块和图像恢复模块。这些模块的基本块是提出的基于注意力的残差密集块。由于使用了双分支，AFF-Dehazing 可以避免一体学习混合退化，增强低光雾度图像的细节。

更新日期：2021-07-12

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