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A single rolling-shutter (RS) image may be viewed as a row-wise combinationof a sequence of global-shutter (GS) images captured by a (virtual) moving GScamera within the exposure duration. Although RS cameras are widely used, theRS effect causes obvious image distortion especially in the presence of fastcamera motion, hindering downstream computer vision tasks. In this paper, wepropose to invert the RS image capture mechanism, i.e., recovering a continuoushigh framerate GS video from two time-consecutive RS frames. We call this taskthe RS temporal super-resolution (RSSR) problem. The RSSR is a very challengingtask, and to our knowledge, no practical solution exists to date. This paperpresents a novel deep-learning based solution. By leveraging the multi-viewgeometry relationship of the RS imaging process, our learning-based frameworksuccessfully achieves high framerate GS generation. Specifically, three novelcontributions can be identified: (i) novel formulations for bidirectional RSundistortion flows under constant velocity as well as constant accelerationmotion model. (ii) a simple linear scaling operation, which bridges the RSundistortion flow and regular optical flow. (iii) a new mutual conversionscheme between varying RS undistortion flows that correspond to differentscanlines. Our method also exploits the underlying spatial-temporal geometricrelationships within a deep learning framework, where no additional supervisionis required beyond the necessary middle-scanline GS image. Building upon thesecontributions, we represent the very first rolling-shutter temporalsuper-resolution deep-network that is able to recover high framerate GS videosfrom just two RS frames. Extensive experimental results on both synthetic andreal data show that our proposed method can produce high-quality GS imagesequences with rich details, outperforming the state-of-the-art methods.

Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video