Feature attention gated context aggregation network for single image dehazing and its application on unmanned aerial vehicle images

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yongquan Wu, Xuan Zhao, Xinsheng Zhang, Tao Long, Ping Luo
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Abstract

Single-image dehazing is a highly challenging ill-posed task in the field of computer vision. To address this, a new image dehazing model with feature attention, named feature attention gated context aggregation network (FAGCA-Net), is proposed to tackle the issues of incomplete or over-dehazing caused by the original model's inability to handle non-uniform haze density distributions. A feature attention module that combines channel attention and spatial attention is introduced. Additionally, the authors propose a new extended attention convolutional block, which not only addresses the grid artefacts caused by the extended convolution but also provides added flexibility in handling different types of feature information. At the same time, in addition to the input image itself, incorporating the dark channel and edge channel of the image as the final input of the model is helpful for the model learning process. To demonstrate the robustness of the new model, it is applied to two completely different dehazing datasets, and it achieves significant dehazing performance improvement over the original model. Finally, to verify the effectiveness of the model in practical production processes, the authors apply it as an image preprocessing step to a set of UAV (Unmanned Aerial Vehicle) images of foreign objects. The result shows that the UAV images after being processed by FAGCA-Net for haze removal have a better impact on subsequent usage.

Abstract Image

用于单一图像去毛刺的特征注意门控上下文聚合网络及其在无人机图像上的应用
单幅图像去毛刺是计算机视觉领域一项极具挑战性的难题。为了解决这个问题,我们提出了一种新的带有特征注意的图像去毛刺模型,命名为特征注意门控上下文聚合网络(FAGCA-Net),以解决由于原始模型无法处理非均匀雾密度分布而导致的不完全去毛刺或过度去毛刺问题。作者还引入了一个结合了通道注意力和空间注意力的特征注意力模块。此外,作者还提出了一种新的扩展注意力卷积块,不仅解决了扩展卷积造成的网格伪影问题,还为处理不同类型的特征信息提供了更大的灵活性。同时,除了输入图像本身,将图像的暗色通道和边缘通道作为模型的最终输入也有助于模型的学习过程。为了证明新模型的鲁棒性,我们将其应用于两个完全不同的去毛刺数据集,结果发现新模型的去毛刺性能明显优于原始模型。最后,为了验证该模型在实际生产过程中的有效性,作者将其作为图像预处理步骤,应用于一组异物的无人机(UAV)图像。结果表明,经过 FAGCA-Net 除雾处理后的无人机图像对后续使用有更好的影响。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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