穿透模糊：应对脉冲成像中的散焦问题
Seeing Through Blur: Tackling Defocus in Spike-Based Imaging
把散焦形成过程用薄透镜近似显式建模成可学习的离散 PSF 先验，再配合多时序尺度 IF 神经元提取脉冲特征，为事件/脉冲类传感器上的空间变化去模糊提供了一个可复用的物理引导复原范式。
原文依据 we present DeSpike, the first end-to-end defocus removal framework specifically designed for spike cameras
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论脉冲相机是一类新型神经形态视觉传感器，通过脉冲平面以超高时序分辨率捕捉场景动态。尽管近期方法已解决脉冲重建中的运动模糊与噪声问题，但由浅景深或镜头调整延迟引起的散焦模糊，在自动驾驶等实际应用中仍是一个关键却研究不足的问题。本文提出 DeSpike，这是首个专为脉冲相机设计的端到端散焦去除框架。该方法首先利用受物理启发的薄透镜近似显式建模散焦形成过程，以模拟光学模糊下的脉冲响应。在该建模的引导下，DeSpike 采用多时序尺度积分发放（IF）神经元补偿 FPN，并从脉冲流中提取散焦感知特征。随后，这些特征由基于可学习离散 PSF 先验构建的物理引导去模糊模块处理。为应对空间变化模糊，我们引入基于 Transformer 的融合机制，通过跨散焦级别的注意力自适应地加权多尺度去模糊结果。最后，由粗到细的迭代精化阶段结合脉冲特征与 PSF 先验进行渐进式复原。在合成与真实散焦脉冲数据集上的大量实验表明，本方法在结构保真度、感知锐度与对比度方面均优于最优去模糊方法，为散焦感知的脉冲图像重建树立了新的基准。
英文原文摘要 1617 字符
Spike cameras are a novel class of neuromorphic vision sensors that capture scene dynamics with ultra-high temporal resolution via spike planes. While recent methods have addressed motion blur and noise in spike-based reconstruction, defocus blur caused by shallow depth of field or lens adjustment delays remains a critical yet underexplored issue in real-world applications such as autonomous driving. In this work, we present DeSpike, the first end-to-end defocus removal framework specifically designed for spike cameras. Our method begins by explicitly modeling the defocus formation process using a physics-inspired thin-lens approximation to simulate spike responses under optical blur. Guided by this formulation, DeSpike employs multi-temporal-scale integrate-and-fire (IF) neurons to compensate for FPN and extract defocus-aware features from spike streams. These features are then processed by a physics-informed deblurring module constructed from learnable discrete PSF priors. To address spatially variant blur, we introduce a Transformer-based fusion mechanism that adaptively weighs multi-scale deblurring results through attention across defocus levels. Finally, a coarse-to-fine iterative refinement stage combines spike features and PSF priors for progressive restoration. Extensive experiments on both synthetic and real-world defocused spike datasets demonstrate that our method achieves superior performance over state-of-the-art deblurring approaches in terms of structural fidelity, perceptual sharpness, and contrast, setting a new benchmark for defocus-aware spike-based image reconstruction.