  Super-Resolution from a single motion Blurred image (SRB) is a severely
ill-posed problem due to the joint degradation of motion blurs and low spatial
resolution. In this paper, we employ events to alleviate the burden of SRB and
propose an Event-enhanced SRB (E-SRB) algorithm, which can generate a sequence
of sharp and clear images with High Resolution (HR) from a single blurry image
with Low Resolution (LR). To achieve this end, we formulate an event-enhanced
degeneration model to consider the low spatial resolution, motion blurs, and
event noises simultaneously. We then build an event-enhanced Sparse Learning
Network (eSL-Net++) upon a dual sparse learning scheme where both events and
intensity frames are modeled with sparse representations. Furthermore, we
propose an event shuffle-and-merge scheme to extend the single-frame SRB to the
sequence-frame SRB without any additional training process. Experimental
results on synthetic and real-world datasets show that the proposed eSL-Net++
outperforms state-of-the-art methods by a large margin. Datasets, codes, and
more results are available at https://github.com/ShinyWang33/eSL-Net-Plusplus.
