来自事件焦点堆栈的全焦成像

Hanyue Lou, Minggui Teng, Yixin Yang, Boxin Shi
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引用次数: 0

摘要

传统的焦点叠加方法需要多次拍摄同一场景中不同距离聚焦的图像,不能很好地应用于动态场景。由于单张图像的散焦和去模糊问题的高度病态性质,从一张照片中生成高质量的全焦图像是具有挑战性的。在本文中,为了恢复全焦图像,我们提出了事件焦点堆栈,它被定义为在连续焦点扫描期间捕获的事件流。给定任意距离聚焦的RGB图像,我们探索事件流的高时间分辨率,从中自动选择重新聚焦的时间戳,并将相应的重新聚焦的图像与事件重建以形成焦点堆栈。在所选时间戳周围相邻事件的引导下,我们可以将焦点堆栈与适当的权重合并,并恢复清晰的全焦图像。在合成数据集和真实数据集上的实验结果都显示出优于最先进方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
All-in-Focus Imaging from Event Focal Stack
Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods.
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