Neural Sensors: Learning Pixel Exposures for HDR Imaging and Video Compressive Sensing with Programmable Sensors

Research output: Contribution to journalArticlepeer-review

  • Authors:
  • Julien N.P. Martel
  • Lorenz K. Muller
  • Stephen J. Carey
  • Piotr Dudek
  • Gordon Wetzstein


Camera sensors rely on global or rolling shutter functions to expose an image. This fixed function approach severely limits the sensors' ability to capture high-dynamic-range (HDR) scenes and resolve high-speed dynamics. Spatially varying pixel exposures have been introduced as a powerful computational photography approach to optically encode irradiance on a sensor and computationally recover additional information of a scene, but existing approaches rely on heuristic coding schemes and bulky spatial light modulators to optically implement these exposure functions. Here, we introduce neural sensors as a methodology to optimize per-pixel shutter functions jointly with a differentiable image processing method, such as a neural network, in an end-to-end fashion. Moreover, we demonstrate how to leverage emerging programmable and re-configurable sensor-processors to implement the optimized exposure functions directly on the sensor. Our system takes specific limitations of the sensor into account to optimize physically feasible optical codes and we evaluate its performance for snapshot HDR and high-speed compressive imaging both in simulation and experimentally with real scenes.

Bibliographical metadata

Original languageEnglish
Article number9064896
Pages (from-to)1642-1653
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number7
Early online date13 Apr 2020
Publication statusPublished - 1 Jul 2020