FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
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DOI：
10.1609/aaai.v34i07.6865
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发表时间：
2019-11
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影响因子：
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通讯作者：
Xu Qin;Zhiling Wang;Yuanchao Bai;Xiaodong Xie;Huizhu Jia
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文献类型：
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作者：
Xu Qin;Zhiling Wang;Yuanchao Bai;Xiaodong Xie;Huizhu Jia
本文提出了一种端到端特征融合网络(FFA-Net)来直接恢复无雾霾图像。FFA-Net结构由三个关键部分组成：1)考虑到不同通道的特征包含完全不同的加权信息，并且不同图像像素上的雾度分布不均匀，提出了一种新的特征注意模块，该模块结合了通道注意和像素注意机制。FA不平等地对待不同的特征和像素，这为处理不同类型的信息提供了额外的灵活性，扩展了CNN的表示能力。2)一个基本的块结构由局部残差学习和特征注意组成，局部残差学习通过多个局部残差连接绕过薄雾区域或低频等不太重要的信息，让主网络结构专注于更有效的信息。3)基于注意力的不同层次特征融合(FFA)结构，从特征注意(FA)模块自适应地学习特征权重，赋予重要特征更多的权重。实验结果表明，在SOTS室内测试数据集上，我们提出的FFA-Net在数量和质量上都大大超过了以往最先进的单幅图像去噪方法，将最好的PSNR度量从30.23db提高到36.39db。代码已在GitHub上提供。
In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components:1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers.The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23 dB to 36.39 dB on the SOTS indoor test dataset. Code has been made available at GitHub.