Pix2HDR -- A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos

• URL: http://arxiv.org/abs/2310.16139v2
• Date: Thu, 25 Apr 2024 16:11:40 GMT
• Title: Pix2HDR -- A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos
• Authors: Caixin Wang, Jie Zhang, Matthew A. Wilson, Ralph Etienne-Cummings,
• Abstract summary: High-speed high dynamic range () 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 for HDR fusion algorithms. Our method greatly enhances the vision system's adaptability and performance in dynamic conditions.
• Score: 2.275097126764287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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