  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 monochrome 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.
