Accurately capturing dynamic scenes with wide-ranging motion and light
intensity is crucial for many vision applications. However, acquiring
high-speed high dynamic range (HDR) video is challenging because the camera's
frame rate restricts its dynamic range. Existing methods sacrifice speed to
acquire multi-exposure frames. Yet, misaligned motion in these frames can still
pose complications for HDR fusion algorithms, resulting in artifacts. Instead
of frame-based exposures, we sample the videos using individual pixels at
varying exposures and phase offsets. Implemented on a pixel-wise programmable
image sensor, our sampling pattern simultaneously captures fast motion at a
high dynamic range. We then transform pixel-wise outputs into an HDR video
using end-to-end learned weights from deep neural networks, achieving high
spatiotemporal resolution with minimized motion blurring. We demonstrate
aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under
low-light conditions and against bright backgrounds - both challenging
conditions for conventional cameras. By combining the versatility of pixel-wise
sampling patterns with the strength of deep neural networks at decoding complex
scenes, our method greatly enhances the vision system's adaptability and
performance in dynamic conditions.

通过使用像素级抽样模式和深度神经网络从动态场景中捕获高速高动态范围的视频，我们的方法在解码复杂场景时结合了像素级抽样模式的灵活性和深度神经网络的优势，大大提升了视觉系统在动态条件下的适应性和性能。