标题： 盲时差成像：未知核的连续稀疏反卷积 显示英文标题

标题： Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels

摘要： 近年来，计算式飞行时间（ToF）成像作为一种令人兴奋且新颖的成像方式崭露头角，它能够为自然场景提供新的、有力的解读，并且其应用范围扩展到了三维、光飞行和非视距成像。 从数学上讲，ToF成像依赖于算法上的超分辨技术，因为后向散射的稀疏光回波位于比数字设备能捕捉到的更高的时间分辨率上。 传统方法需要知道发射的光脉冲或核，并使用稀疏反卷积来恢复场景。 与之前的方法不同，本文介绍了一种新的盲ToF成像技术，这种技术不需要核校准，并且能够在连续域而不是离散网格中恢复稀疏尖峰。 通过研究各种ToF模式的共同特征，我们利用了大多数物理脉冲近似满足逼近理论中的Strang-Fix条件这一事实。 这导致了一个新的稀疏超分辨数学公式。 我们的恢复方法使用了一种基于交替最小化策略的优化方法。 我们将我们的盲ToF方法与传统的核校准方法进行了基准测试，后者作为基线。 广泛的硬件实验跨越不同的ToF模式，展示了我们方法在算法上的优势、灵活性和实证鲁棒性。 我们表明，我们的工作促进了在区分近距离物体具有挑战性的场景中的超分辨能力，同时保持了与已知核情况相当的性能。 光飞行成像和光扫描视频的例子突显了我们盲超分辨方法在增强对自然场景理解方面的实际好处。 显示英文摘要

摘要： In recent years, computational Time-of-Flight (ToF) imaging has emerged as an exciting and a novel imaging modality that offers new and powerful interpretations of natural scenes, with applications extending to 3D, light-in-flight, and non-line-of-sight imaging. Mathematically, ToF imaging relies on algorithmic super-resolution, as the back-scattered sparse light echoes lie on a finer time resolution than what digital devices can capture. Traditional methods necessitate knowledge of the emitted light pulses or kernels and employ sparse deconvolution to recover scenes. Unlike previous approaches, this paper introduces a novel, blind ToF imaging technique that does not require kernel calibration and recovers sparse spikes on a continuum, rather than a discrete grid. By studying the shared characteristics of various ToF modalities, we capitalize on the fact that most physical pulses approximately satisfy the Strang-Fix conditions from approximation theory. This leads to a new mathematical formulation for sparse super-resolution. Our recovery approach uses an optimization method that is pivoted on an alternating minimization strategy. We benchmark our blind ToF method against traditional kernel calibration methods, which serve as the baseline. Extensive hardware experiments across different ToF modalities demonstrate the algorithmic advantages, flexibility and empirical robustness of our approach. We show that our work facilitates super-resolution in scenarios where distinguishing between closely spaced objects is challenging, while maintaining performance comparable to known kernel situations. Examples of light-in-flight imaging and light-sweep videos highlight the practical benefits of our blind super-resolution method in enhancing the understanding of natural scenes.