Image Classification in the Dark using Quanta Image Sensors
- URL: http://arxiv.org/abs/2006.02026v3
- Date: Thu, 16 Jul 2020 20:22:46 GMT
- Title: Image Classification in the Dark using Quanta Image Sensors
- Authors: Abhiram Gnanasambandam and Stanley H. Chan
- Abstract summary: We present a new low-light image classification solution using Quanta Image Sensors (QIS)
QIS are a new type of image sensors that possess photon counting ability without compromising on pixel size and spatial resolution.
We show that with student-teacher learning, we are able to achieve image classification at a photon level of one photon per pixel or lower.
- Score: 17.931673459050792
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: State-of-the-art image classifiers are trained and tested using
well-illuminated images. These images are typically captured by CMOS image
sensors with at least tens of photons per pixel. However, in dark environments
when the photon flux is low, image classification becomes difficult because the
measured signal is suppressed by noise. In this paper, we present a new
low-light image classification solution using Quanta Image Sensors (QIS). QIS
are a new type of image sensors that possess photon counting ability without
compromising on pixel size and spatial resolution. Numerous studies over the
past decade have demonstrated the feasibility of QIS for low-light imaging, but
their usage for image classification has not been studied. This paper fills the
gap by presenting a student-teacher learning scheme which allows us to classify
the noisy QIS raw data. We show that with student-teacher learning, we are able
to achieve image classification at a photon level of one photon per pixel or
lower. Experimental results verify the effectiveness of the proposed method
compared to existing solutions.
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