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Aerial image dehazing using a deep convolutional autoencoder

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Abstract

Aerial images and videos are extensively used for object detection and target tracking. However, due to the presence of thin clouds, haze or smoke from buildings, the processing of aerial data can be challenging. Existing single-image dehazing methods that work on ground-to-ground images, do not perform well on aerial images. Moreover, current dehazing methods are not capable for real-time processing. In this paper, a new end-to-end aerial image dehazing method using a deep convolutional autoencoder is proposed. Using the convolutional autoencoder, the dehazing problem is divided into two parts, namely, encoder, which aims extract important features to dehaze hazy regions and decoder, which aims to reconstruct the dehazed image using the down-sampled image received from the encoder. In this proposed method, we also exploit the superpixels in two different scales to generate synthetic thin cloud data to train our network. Since this network is trained in an end-to-end manner, in the test phase, for each input hazy aerial image, the proposed algorithm outputs a dehazed version without requiring any other information such as transmission map or atmospheric light value. With the proposed method, hazy regions are dehazed and objects within hazy regions become more visible while the contrast of non-hazy regions is increased. Experimental results on synthetic and real hazy aerial images demonstrate the superiority of the proposed method compared to existing dehazing methods in terms of quality and speed.

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Authors and Affiliations

  1. ECE Department of McMaster University, 1280 Main Street West, Hamilton, ON, Canada

    Hamidreza Fazlali, Shahram Shirani, Mike McDonald, Daly Brown & Thia Kirubarajan

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  1. Hamidreza Fazlali
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  2. Shahram Shirani
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  3. Mike McDonald
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  5. Thia Kirubarajan
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Correspondence to Hamidreza Fazlali.

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Fazlali, H., Shirani, S., McDonald, M. et al. Aerial image dehazing using a deep convolutional autoencoder. Multimed Tools Appl 79, 29493–29511 (2020). https://doi.org/10.1007/s11042-020-09383-7

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  • DOI: https://doi.org/10.1007/s11042-020-09383-7

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