  Super-resolution from motion-blurred images poses a significant challenge due
to the combined effects of motion blur and low spatial resolution. To address
this challenge, this paper introduces an Event-based Blurry Super Resolution
Network (EBSR-Net), which leverages the high temporal resolution of events to
mitigate motion blur and improve high-resolution image prediction.
Specifically, we propose a multi-scale center-surround event representation to
fully capture motion and texture information inherent in events. Additionally,
we design a symmetric cross-modal attention module to fully exploit the
complementarity between blurry images and events. Furthermore, we introduce an
intermodal residual group composed of several residual dense Swin Transformer
blocks, each incorporating multiple Swin Transformer layers and a residual
connection, to extract global context and facilitate inter-block feature
aggregation. Extensive experiments show that our method compares favorably
against state-of-the-art approaches and achieves remarkable performance.
