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Deep learning to enable color vision in the dark.

Andrew W Browne1,2,3, Ekaterina Deyneka4,5, Francesco Ceccarelli4,5

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Researchers developed a deep learning algorithm to predict visible light images from infrared illumination. This technology could enable digital rendering of scenes in complete darkness for enhanced night vision applications.

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Area of Science:

  • Computer Vision
  • Optics
  • Artificial Intelligence

Background:

  • Human vision is limited to the visible light spectrum (400-700 nm).
  • Current night vision systems often use infrared light, rendering monochromatic images not naturally perceived by humans.
  • There is a need for systems that can translate imperceptible infrared illumination into human-visible spectrum representations.

Purpose of the Study:

  • To develop an imaging algorithm using deep learning to predict visible spectrum renderings from infrared illumination.
  • To enable digital visualization of scenes in conditions of complete darkness where only infrared light is present.
  • To bridge the gap between infrared imaging and human visual perception.

Main Methods:

  • Acquired a dataset of printed images under multispectral illumination, including visible (red, green, blue) and near-infrared wavelengths (718, 777, 807 nm).
  • Utilized a monochromatic camera sensitive to both visible and near-infrared light.
  • Optimized a convolutional neural network with a U-Net-like architecture for image prediction.

Main Results:

  • Successfully trained a deep learning model to predict visible spectrum images from near-infrared input.
  • Demonstrated the feasibility of rendering scenes perceived in the visible spectrum using only infrared illumination.
  • Achieved accurate prediction of visible spectrum renderings from infrared images.

Conclusions:

  • This study represents a significant first step towards predicting human-visible spectrum scenes from imperceptible near-infrared illumination.
  • The developed algorithm holds potential for diverse applications, including advanced night vision technologies.
  • Further research can enhance this approach for biological studies and other fields requiring visible light interpretation from infrared data.