EvShutter: Transforming Events for Unconstrained Rolling Shutter Correction

Julius Erbach, Stepan Tulyakov, Patricia Vitoria, Alfredo Bochicchio, Yuanyou Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 13904-13913

Widely used Rolling Shutter (RS) CMOS sensors capture high resolution images at the expense of introducing distortions and artifacts in the presence of motion. In such situations, RS distortion correction algorithms are critical. Recent methods rely on a constant velocity assumption and require multiple frames to predict the dense displacement field. In this work, we introduce a new method, called Eventful Shutter (EvShutter), that corrects RS artifacts using a single RGB image and event information with high temporal resolution. The method firstly removes blur using a novel flow-based deblurring module and then compensates RS using a double encoder hourglass network. In contrast to previous methods, it does not rely on a constant velocity assumption and uses a simple architecture thanks to an event transformation dedicated to RS, called Filter and Flip (FnF), that transforms input events to encode only the changes between GS and RS images. To evaluate the proposed method and facilitate future research, we collect the first dataset with real events and high-quality RS images with optional blur, called RS-ERGB. We generate the RS images from GS images using a newly proposed simulator based on adaptive interpolation. The simulator permits the use of inexpensive cameras with long exposure to capture high-quality GS images. We show that on this realistic dataset the proposed method outperforms the state-of-the-art image- and event-based methods by 9.16 dB and 0.75 dB respectively in terms of PSNR and an improvement of 23% and 21% in LPIPS.