  A camera begins to sense light the moment we press the shutter button. During
the exposure interval, relative motion between the scene and the camera causes
motion blur, a common undesirable visual artifact. This paper presents E-CIR,
which converts a blurry image into a sharp video represented as a parametric
function from time to intensity. E-CIR leverages events as an auxiliary input.
We discuss how to exploit the temporal event structure to construct the
parametric bases. We demonstrate how to train a deep learning model to predict
the function coefficients. To improve the appearance consistency, we further
introduce a refinement module to propagate visual features among consecutive
frames. Compared to state-of-the-art event-enhanced deblurring approaches,
E-CIR generates smoother and more realistic results. The implementation of
E-CIR is available at https://github.com/chensong1995/E-CIR.
