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Photographs taken through a window are often compromised by dirt or rain
present on the window surface. Common cases of this include pictures taken
from inside a vehicle, or outdoor security cameras mounted inside a protective
enclosure. At capture time, defocus can be used to remove the artifacts, but
this relies on achieving a shallow depth-of-field and placement of the camera
close to the window. Instead, we present a post-capture image processing
solution that can remove localized rain and dirt artifacts from a single image.
We collect a dataset of clean/corrupted image pairs which are then used to
train a specialized form of convolutional neural network. This learns how to
map corrupted image patches to clean ones, implicitly capturing the
characteristic appearance of dirt and water droplets in natural images. Our
models demonstrate effective removal of dirt and rain in outdoor test
conditions.
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