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We propose a simple, cost-effective method for synchronized phase contrast and fluorescence video acquisition in live samples. Counter-phased pulses of phase contrast illumination and fluorescence excitation light are synchronized with the exposure of the two fields of an interlaced camera sensor. This results in a video sequence in which each frame contains both exposure modes, each in half of its pixels. The method allows real-time acquisition and display of synchronized and spatially aligned phase contrast and fluorescence image sequences that can be separated by de-interlacing in two independent videos. The method can be implemented on any fluorescence microscope with a camera port without needing to modify the optical path.
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This content will become publicly available on June 2, 2026
SNAPPIX: Efficient-Coding–Inspired In-Sensor Compression for Edge Vision
Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabilities and must transmit data to a remote server/cloud for processing. To reduce the edge energy consumption, this paper proposes a sensor-algorithm co-designed system called SNAPPIX, which compresses raw pixels in the analog domain inside the sensor. We use coded exposure (CE) as the in-sensor compression strategy as it offers the flexibility to sample, i.e., selectively expose pixels, both spatially and temporally. SNAPPIX has three contributions. First, we propose a task-agnostic strategy to learn the sampling/exposure pattern based on the classic theory of efficient coding. Second, we co- design the downstream vision model with the exposure pattern to address the pixel-level non-uniformity unique to CE-compressed images. Finally, we propose lightweight augmentations to the image sensor hardware to support our in-sensor CE compres- sion. Evaluating on action recognition and video reconstruction, SNAPPIX outperforms state-of-the-art video-based methods at the same speed while reducing the energy by up to 15.4×. We have open-sourced the code at: https://github.com/horizon- research/SnapPix.
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- Award ID(s):
- 2328856
- PAR ID:
- 10633385
- Publisher / Repository:
- ACM/IEEE DAC 2025
- Date Published:
- ISSN:
- 0738-100X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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