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Learning to recover clear images from images having a combination ofdegrading factors is a challenging task. That being said, autonomoussurveillance in low visibility conditions caused by high pollution/smoke, poorair quality index, low light, atmospheric scattering, and haze during ablizzard becomes even more important to prevent accidents. It is thus crucialto form a solution that can result in a high-quality image and is efficientenough to be deployed for everyday use. However, the lack of proper datasetsavailable to tackle this task limits the performance of the previous methodsproposed. To this end, we generate the LowVis-AFO dataset, containing 3647paired dark-hazy and clear images. We also introduce a lightweight deeplearning model called Low-Visibility Restoration Network (LVRNet). Itoutperforms previous image restoration methods with low latency, achieving aPSNR value of 25.744 and an SSIM of 0.905, making our approach scalable andready for practical use. The code and data can be found athttps://github.com/Achleshwar/LVRNet.

LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility