Abstract
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.
Keywords
DirtWindow (computing)Computer scienceArtificial intelligenceComputer visionConvolutional neural networkImage (mathematics)Remote sensingGeologyGeographyCartography
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Publication Info
- Year
- 2013
- Type
- article
- Pages
- 633-640
- Citations
- 465
- Access
- Closed
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Cite This
David Eigen,
Dilip Krishnan,
Rob Fergus
(2013).
Restoring an Image Taken through a Window Covered with Dirt or Rain.
, 633-640.
https://doi.org/10.1109/iccv.2013.84
Identifiers
- DOI
- 10.1109/iccv.2013.84