Skip to main content
Log in

Pixel-correlated computing for detecting and tracking targets in dim lighting

  • Research Briefing
  • Published:

From Nature Electronics

View current issue Submit your manuscript

An approach to dynamically control the photoresponsivity of pixels in a computational sensor based on local image gradients enables the precise and robust detection of edge features of targets in dim light conditions from a single image capture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1: A conceptual illustration and experimental demonstration of pixel-correlated computing.

References

  1. Gouveia, L. C. P. & Choubey, B. Advances on CMOS image sensors. Sensor Rev. 36, 231–239 (2016). An introduction to the technological advances of CMOS image sensors.

    Article  Google Scholar 

  2. Wang, C.-Y. et al. Gate-tunable van der Waals heterostructure for reconfigurable neural network vision sensor. Sci. Adv. 6, eaba6173 (2020). This paper reports a bioinspired retinomorphic vision sensor.

    Article  Google Scholar 

  3. Liu, G. et al. Graphene-assisted metal transfer printing for wafer-scale integration of metal electrodes and two-dimensional materials. Nat. Electron. 5, 275–280 (2022). This paper reports a general approach for the large-scale integration of devices based on arrays of low-dimensional materials.

    Article  Google Scholar 

  4. Akinwande, D. et al. Graphene and two-dimensional materials for silicon technology. Nature 573, 507–518 (2019). A review article that presents the progress and challenges of integrating two-dimensional materials with silicon-based nanosystems.

    Article  Google Scholar 

  5. Yuan, S. et al. Geometric deep optical sensing. Science 379, eade1220 (2023). This review article outlines the advances in optical sensing and imaging achieved with reconfigurable sensors.

    Article  Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Yang, Y. et al. In-sensor dynamic computing for intelligent machine vision. Nat. Electron. https://doi.org/10.1038/s41928-024-01124-0 (2024).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pixel-correlated computing for detecting and tracking targets in dim lighting. Nat Electron 7, 191–192 (2024). https://doi.org/10.1038/s41928-024-01125-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-024-01125-z

  • Springer Nature Limited

Navigation