SUNet: Symmetric Undistortion Network for Rolling Shutter Correction
This addresses image quality issues for users of consumer-grade cameras, representing an incremental improvement over existing methods.
The paper tackles rolling shutter distortion in consumer cameras by introducing a symmetric deep network that uses two consecutive frames to predict a global shutter image at an intermediate time, achieving state-of-the-art results in experiments on synthetic and real data.
The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism, leading to image distortions if the camera moves during image acquisition. In this paper, we present a novel deep network to solve the generic rolling shutter correction problem with two consecutive frames. Our pipeline is symmetrically designed to predict the global shutter image corresponding to the intermediate time of these two frames, which is difficult for existing methods because it corresponds to a camera pose that differs most from the two frames. First, two time-symmetric dense undistortion flows are estimated by using well-established principles: pyramidal construction, warping, and cost volume processing. Then, both rolling shutter images are warped into a common global shutter one in the feature space, respectively. Finally, a symmetric consistency constraint is constructed in the image decoder to effectively aggregate the contextual cues of two rolling shutter images, thereby recovering the high-quality global shutter image. Extensive experiments with both synthetic and real data from public benchmarks demonstrate the superiority of our proposed approach over the state-of-the-art methods.