  Video frames captured by rolling shutter (RS) cameras during fast camera
movement frequently exhibit RS distortion and blur simultaneously. Naturally,
recovering high-frame-rate global shutter (GS) sharp frames from an RS blur
frame must simultaneously consider RS correction, deblur, and frame
interpolation. A naive way is to decompose the whole process into separate
tasks and cascade existing methods; however, this results in cumulative errors
and noticeable artifacts. Event cameras enjoy many advantages, e.g., high
temporal resolution, making them potential for our problem. To this end, we
propose the first and novel approach, named UniINR, to recover arbitrary
frame-rate sharp GS frames from an RS blur frame and paired events. Our key
idea is unifying spatial-temporal implicit neural representation (INR) to
directly map the position and time coordinates to color values to address the
interlocking degradations. Specifically, we introduce spatial-temporal implicit
encoding (STE) to convert an RS blur image and events into a spatial-temporal
representation (STR). To query a specific sharp frame (GS or RS), we embed the
exposure time into STR and decode the embedded features pixel-by-pixel to
recover a sharp frame. Our method features a lightweight model with only 0.38M
parameters, and it also enjoys high inference efficiency, achieving
2.83ms/frame in 31 times frame interpolation of an RS blur frame. Extensive
experiments show that our method significantly outperforms prior methods. Code
is available at https://github.com/yunfanLu/UniINR.
