SciDVS: A Scientific Event Camera with 1.7% Temporal Contrast Sensitivity at 0.7 lux
Rui Graca, Sheng Zhou, Brian McReynolds, Tobi Delbruck

TL;DR
This paper introduces SciDVS, a highly sensitive scientific event camera that operates effectively in low-light conditions, achieving 1.7% sensitivity at 0.7 lux with novel in-pixel amplification and adjustable resolution.
Contribution
The paper presents a new DVS prototype with significantly enhanced sensitivity and HDR capabilities, utilizing novel in-pixel preamplifiers and adjustable pixel binning for scientific applications.
Findings
Achieves 1.7% sensitivity at 0.7 lux illumination.
Provides intrascene HDR and increased sensitivity.
Enables trade-off between spatial resolution and sensitivity.
Abstract
This paper reports a Dynamic Vision Sensor (DVS) event camera that is 6x more sensitive at 14x lower illumination than existing commercial and prototype cameras. Event cameras output a sparse stream of brightness change events. Their high dynamic range (HDR), quick response, and high temporal resolution provide key advantages for scientific applications that involve low lighting conditions and sparse visual events. However, current DVS are hindered by low sensitivity, resulting from shot noise and pixel-to-pixel mismatch. Commercial DVS have a minimum brightness change threshold of >10%. Sensitive prototypes achieved as low as 1%, but required kilo-lux illumination. Our SciDVS prototype fabricated in a 180nm CMOS image sensor process achieves 1.7% sensitivity at chip illumination of 0.7 lx and 18 Hz bandwidth. Novel features of SciDVS are (1) an auto-centering in-pixel preamplifier…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Radiography and Breast Imaging · Radiation Detection and Scintillator Technologies · Scientific Computing and Data Management
