EVUNROLL: NEUROMORPHIC EVENTS BASED ROLLING SUTTER IMAGE CORRECTION
PEKING UNIVERSITY, BEIJING ACADEMY OF ARTIFICIAL INTELLIGENCE
Xinyu Zhou, Peiqi Duan, Yi Ma, Boxin Shi
ABSTRACT
This paper proposes to use neuromorphic events for correcting rolling shutter (RS) images as consecutive global shutter (GS) frames. RS effect introduces edge distortion and region occlusion into images caused by row-wise readout of CMOS sensors. We introduce a novel computational imaging setup consisting of an RS sensor and an event sensor, and propose a neural network called EvUnroll to solve this problem by exploring the high-temporal-resolution property of events. We use events to bridge a spatio-temporal connection between RS and GS, establish a flow estimation module to correct edge distortions, and design a synthesis-based restoration module to restore occluded regions. The results of two branches are fused through a refining module to generate corrected GS images. We further propose datasets captured by a high-speed camera and an RS-Event hybrid camera system for training and testing our network. Experimental results on both public and proposed datasets show a systematic performance improvement compared to state-of-the-art methods.
