  Temperature difference-induced mist adhered to the glass, such as windshield,
camera lens, is often inhomogeneous and obscure, easily obstructing the vision
and severely degrading the image. Together with adherent raindrops, they bring
considerable challenges to various vision systems but without enough attention.
Recent methods for other similar problems typically use hand-crafted priors to
generate spatial attention maps. In this work, we newly present a problem of
image degradation caused by adherent mist and raindrops. An attentive
convolutional network is adopted to visually remove the adherent mist and
raindrop from a single image. A baseline architecture with general channel-wise
attention, spatial attention, and multilevel feature fusion is used.
Considering the variations and regional characteristics of adherent mist and
raindrops, we apply interpolation-based pyramid-attention blocks to perceive
spatial information at different scales. Experiments show that the proposed
method can improve severely degraded images' visibility, both qualitatively and
quantitatively. More applied experiments show that this underrated practical
problem is critical to high-level vision scenes. Our method also achieves
state-of-the-art performance on conventional dehazing and pure de-raindrop
problems, in addition to our task of handling adherent mist and raindrops.
