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}.

翻译：从模糊输入中重建一系列清晰图像，对于增强我们对所捕捉场景的理解至关重要，但由于图像中嵌入的时间特征有限，这一任务具有显著挑战性。脉冲相机以高达40,000 Hz的采样率，已被证明在捕捉运动特征方面有效，且有助于解决这一病态问题。然而，现有方法属于监督学习范式，当应用于与合成训练数据域存在差异的真实场景时，会出现显著的性能下降。此外，重建图像的质量受限于基于运动分析插值生成的图像，这些图像本质上有别于实际场景，从而影响这些方法在真实高速场景中的泛化能力。为解决这些挑战，我们提出了首个用于脉冲引导运动去模糊任务的自监督框架。我们的方法首先构建了一个脉冲引导去模糊模型，探讨脉冲流、模糊图像及其对应清晰序列之间的理论关系。随后，我们开发了一个自监督级联框架以缓解去模糊模型中遇到的脉冲噪声和空间分辨率不匹配问题。通过知识蒸馏和重模糊损失，我们进一步设计了一个轻量级去模糊网络，以生成与原始输入在亮度和纹理上保持一致的高质量序列。在我们真实和合成数据集上进行的定量与定性实验（含脉冲数据）验证了所提框架的优越泛化能力。我们的代码、数据和训练模型将在\url{https://github.com/chenkang455/S-SDM}上提供。