Single-Image Rolling Shutter Correction refers to a process or technique used to mitigate the distortions introduced in images captured by cameras equipped with rolling shutter mechanisms. Rolling shutter is a method used in many digital cameras where the image is captured sequentially, row by row or column by column, rather than all at once. This can lead to distortions, especially when capturing images of fast-moving objects or during rapid camera motion.

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Learning Structure-And-Motion-Aware Rolling Shutter Correction

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An exact method of correcting the rolling shutter (RS) effect requires recovering the underlying geometry, i.e. the scene structures and the camera motions between scanlines or between views. However, the multiple-view geometry for RS cameras is much more complicated than its global shutter (GS) counterpart, with various degeneracies. In this paper, we first make a theoretical contribution by showing that RS two-view geometry is degenerate in the case of pure translational camera motion. In view of the complex RS geometry, we then propose a Convolutional Neural Network (CNN)-based method which learns the underlying geometry (camera motion and scene structure) from just a single RS image and perform RS image correction. We call our method structure-and-motion-aware RS correction because it reasons about the concealed motions between the scanlines as well as the scene structure. Our method learns from a large-scale dataset synthesized in a geometrically meaningful way where the RS effect is generated in a manner consistent with the camera motion and scene structure. In extensive experiments, our method achieves superior performance compared to other state-of-the-art methods for single image RS correction and subsequent Structure from Motion (SfM) applications.

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