  With extremely high temporal resolution, event cameras have a large potential
for robotics and computer vision. However, their asynchronous imaging mechanism
often aggravates the measurement sensitivity to noises and brings a physical
burden to increase the image spatial resolution. To recover high-quality
intensity images, one should address both denoising and super-resolution
problems for event cameras. Since events depict brightness changes, with the
enhanced degeneration model by the events, the clear and sharp high-resolution
latent images can be recovered from the noisy, blurry and low-resolution
intensity observations. Exploiting the framework of sparse learning, the events
and the low-resolution intensity observations can be jointly considered. Based
on this, we propose an explainable network, an event-enhanced sparse learning
network (eSL-Net), to recover the high-quality images from event cameras. After
training with a synthetic dataset, the proposed eSL-Net can largely improve the
performance of the state-of-the-art by 7-12 dB. Furthermore, without additional
training process, the proposed eSL-Net can be easily extended to generate
continuous frames with frame-rate as high as the events.
