Ms. Wenjun Wang Profile

Wenjun Wang

at Beihang (BUAA)

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Proceedings Article | 19 November 2021 Paper

Ground-truth information agnostic deep dehazing network for C919 aircraft image

Wenjun Wang, Yunhao Zhang, Ting-Bing Xu, Zhenzhong Wei

Proceedings Volume 12059, 120591B (2021) https://doi.org/10.1117/12.2612140

KEYWORDS: Image quality, Image processing, Image enhancement, Air contamination, Performance modeling, Image restoration, Visualization, Process modeling, Detection and tracking algorithms, Machine vision

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Aircraft images captured by a third-party camera during take-off and landing can be used for monitoring and aircraft pose measurement. Hazy weather would severely affect the aircraft image quality and incur the worse visual perception. Haze removal from the aircraft image has become an important task for practical industrial applications. Existing deep learning algorithms need the hazy image and corresponding hazy-free ground-truth image simultaneously for the same scene and time, to learn the dehazing process. However, the ground-truth aircraft images are difficult to obtain, which hinders those approaches from addressing the actual aircraft image dehazing problem. In this paper, we present an endto- end ground-truth information agnostic deep dehazing network for single C919 aircraft image dehazing problem. Instead of the requirement of ground-truth image, we train the network only by utilizing the pair of hazy and predehazed images. The pre-dehazed image can be easily obtained by the conventional dehazing manner without deep learning, and the Natural Image Quality Evaluator (NIQE) is introduced to find the best dehazing model. Compared to existing dehazing algorithms, the proposed algorithm can be capable of addressing real-world hazy C919 aircraft images effectively and achieve the best dehazed performance on our collected aircraft dataset.

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