Joint Video Rolling Shutter Correction and Super-Resolution

With the prevalence of CMOS cameras in many computer vision applications, there is an increase in the appearance of rolling shutter (RS) artifacts in captured videos. However, existing video super-resolution algorithms assume that the motion is globally consistent in each video frame and no rolling shutter effect is present. The problem of video super-resolution for video captured using RS cameras is challenging as the model needs to learn the row-wise local pixel displacements and the global structure of the frame for RS correction and super-resolution, simultaneously. Different from existing works, we address a more realistic problem of joint rolling shutter correction and super-resolution (RS-SR). We introduce a novel architecture, deformable Patch Attention Network (PatchNet), that utilizes patch-recurrence property along with deformable receptive fields to learn the global and local structure of the video. Specifically, PatchNet leverages bi-directional motion field in the feature space to extract relevant information from neighboring patches using attention mechanism, and deformable fields using deformable convolutions to extract local pixel-level information for joint rolling shutter correction and super-resolution. Our work is the first to tackle the task of RS correction and super-resolution on the recently released BS-RSCD dataset. Experiments on the BS-RSCD and FastecRS datasets demonstrate that our model performs favorably against various state-of-the-art approaches.