  Reconstructing a sequence of sharp images from the blurry input is crucial
for enhancing our insights into the captured scene and poses a significant
challenge due to the limited temporal features embedded in the image. Spike
cameras, sampling at rates up to 40,000 Hz, have proven effective in capturing
motion features and beneficial for solving this ill-posed problem. Nonetheless,
existing methods fall into the supervised learning paradigm, which suffers from
notable performance degradation when applied to real-world scenarios that
diverge from the synthetic training data domain. Moreover, the quality of
reconstructed images is capped by the generated images based on motion analysis
interpolation, which inherently differs from the actual scene, affecting the
generalization ability of these methods in real high-speed scenarios. To
address these challenges, we propose the first self-supervised framework for
the task of spike-guided motion deblurring. Our approach begins with the
formulation of a spike-guided deblurring model that explores the theoretical
relationships among spike streams, blurry images, and their corresponding sharp
sequences. We subsequently develop a self-supervised cascaded framework to
alleviate the issues of spike noise and spatial-resolution mismatching
encountered in the deblurring model. With knowledge distillation and
re-blurring loss, we further design a lightweight deblur network to generate
high-quality sequences with brightness and texture consistency with the
original input. Quantitative and qualitative experiments conducted on our
real-world and synthetic datasets with spikes validate the superior
generalization of the proposed framework. Our code, data and trained models
will be available at \url{https://github.com/chenkang455/S-SDM}.
