Image Deblurring Aided by Low-Resolution Events

Abstract

Due to the limitation of event sensors, the spatial resolution of event data is relatively low compared to the spatial resolution of the conventional frame-based camera. However, low-spatial-resolution events recorded by event cameras are rich in temporal information which is helpful for image deblurring, while intensity images captured by frame cameras are in high resolution and have potential to promote the quality of events. Considering the complementarity between events and intensity images, an alternately performed model is proposed in this paper to deblur high-resolution images with the help of low-resolution events. This model is composed of two components: a DeblurNet and an EventSRNet. It first uses the DeblurNet to attain a preliminary sharp image aided by low-resolution events. Then, it enhances the quality of events with EventSRNet by extracting the structure information in the generated sharp image. Finally, the enhanced events are sent back into DeblurNet to attain a higher quality intensity image. Extensive evaluations on the synthetic GoPro dataset and real RGB-DAVIS dataset have shown the effectiveness of the proposed method.