Adherent Mist and Raindrop Removal from a Single Image Using Attentive Convolutional Network
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Temperature difference-induced mist adhered to the windshield, camera lens, etc. are often inhomogeneous and obscure, which can easily obstruct the vision and degrade the image severely. Together with adherent raindrops, they bring considerable challenges to various vision systems but without enough attention. Recent methods for similar problems typically use hand-crafted priors to generate spatial attention maps. In this work, we propose to visually remove the adherent mist and raindrop jointly from a single image using attentive convolutional neural networks. We apply classification activation map attention to our model to strengthen the spatial attention without hand-crafted priors. In addition, the smoothed dilated convolution is adopted to obtain a large receptive field without spatial information loss, and the dual attention module is utilized for efficiently selecting channels and spatial features. Our experiments show our method achieves state-of-the-art performance, and demonstrate that this underrated practical problem is critical to high-level vision scenes.
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