Uav Remote Sensing Image Dehazing Based On Saliency Guided Two-Scaletransmission Correction

Zhang, Kemeng; Zheng, Ruohui; Ma, Sijia; Zhang, Libao

doi:10.1109/icip42928.2021.9506123

Cited by 2 publications

(2 citation statements)

References 28 publications

(34 reference statements)

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“…Though above mentioned ASM is suitable for describing the imaging processing of a conventional optics image, it may fail to deal with the imaging of a remote sensing image because of the various of the scene depth of remote sensing images. Usually, the scene depth of a conventional optics image is relatively short, but a remote sensing image usually has a longer scene depth and smaller transmission [31], which is more likely to cause texture loss, colour distortion and insufficient dehazing, due to the uncertainty of the height and inclination angle of remote sensing satellite and UAV in the shooting process, especially the in‐orbit height of remote sensing satellite, for example, the orbital altitude of ESA ERS‐ 2 remote sensing satellite is 780 km.…”

Section: Related Theoriesmentioning

confidence: 99%

A saliency guided remote sensing image dehazing network model

Shi

Shao

Zhou

2022

IET Image Processing

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This article presents a saliency guided remote sensing image dehazing network model. It consists of the following three blocks: A dense residual based backbone network, a saliency map generator, and a deformed atmospheric scattering model (ASM) based haze removal model, of which the dense residual based backbone network is used to capture the texture detail information of a remote sensing image, the saliency map generator is used to generate the saliency map of the related remote sensing image, and the generated saliency map is used to guide the network to capture more texture details through the guided fusion module. Finally, the deformed atmospheric scattering model (ASM) is used to remove haze from remote sensing images. The model here is compared with several state‐of‐art dehazing methods on synthetic data sets and real remote sensing images. Experimental results show that on the synthetic data set, the PSNR value of this model is increased by 4.47 db and the SSIM value is increased by 0.045 compared with the best model. On real remote sensing hazy images, the visual effect of our model is also better than that of existing methods. The authors also perform experiments to demonstrate that remote sensing image dehazing is helpful for remote sensing image detection automatically.

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Section: Related Theoriesmentioning

confidence: 99%

A saliency guided remote sensing image dehazing network model

Shi

Shao

Zhou

2022

IET Image Processing

View full text Add to dashboard Cite

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“…Zhao et al [10] investigated the application of image-defogging technology in UAV visual navigation. Zhang et al [11] studied a defogging method based on significance-guided dual-scale transmission correction. Wang et al [12] utilized the double tangent curve to enhance the brightness of pedestrian photos acquired by UAV in night-time environments.…”

Section: Introductionmentioning

confidence: 99%

Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection

2022

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Aiming at the problem that obstacle avoidance of unmanned aerial vehicles (UAVs) cannot effectively detect obstacles under low illumination, this research proposes an enhancement algorithm for low-light airborne images, which is based on the camera response model and Retinex theory. Firstly, the mathematical model of low-illumination image enhancement is established, and the relationship between the camera response function (CRF) and brightness transfer function (BTF) is constructed by a common parameter equation. Secondly, to solve the problem that the enhancement algorithm using the camera response model will lead to blurred image details, Retinex theory is introduced into the camera response model to design an enhancement algorithm framework suitable for UAV obstacle avoidance. Thirdly, to shorten the time consumption of the algorithm, an acceleration solver is adopted to calculate the illumination map, and the exposure matrix is further calculated via the illumination map. Additionally, the maximum exposure value is set for low signal-to-noise ratio (SNR) pixels to suppress noise. Finally, a camera response model and exposure matrix are used to adjust the low-light image to obtain an enhanced image. The enhancement experiment for the constructed dataset shows that the proposed algorithm can significantly enhance the brightness of low-illumination images, and is superior to other similar available algorithms in quantitative evaluation metrics. Compared with the illumination enhancement algorithm based on infrared and visible image fusion, the proposed algorithm can achieve illumination enhancement without introducing additional airborne sensors. The obstacle object detection experiment shows that the proposed algorithm can increase the AP (average precision) value by 0.556.

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Uav Remote Sensing Image Dehazing Based On Saliency Guided Two-Scaletransmission Correction

Cited by 2 publications

References 28 publications

A saliency guided remote sensing image dehazing network model

A saliency guided remote sensing image dehazing network model

Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection

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