Rating Super-Resolution Microscopy Images With Deep Learning


  • Louis-Émile Robitaille Université Laval
  • Audrey Durand Université Laval
  • Marc-André Gardner Université Laval
  • Christian Gagné Université Laval
  • Paul De Koninck Université Laval
  • Flavie Lavoie-Cardinal Université Laval




deep neural network regression, super-resolution microscopy, image quality


With super-resolution optical microscopy, it is now possible to observe molecular mechanisms. The quality of the obtained images vary a lot depending on the samples and the imaging parameters. Moreover, evaluating this quality is a difficult task. In this work, we want to learn the quality function from scores provided by experts. We propose the use of a deep network that output a quality score for a given image. A user study evaluate the quality of the predictions against human expert scores.




How to Cite

Robitaille, L.- Émile, Durand, A., Gardner, M.-A., Gagné, C., De Koninck, P., & Lavoie-Cardinal, F. (2018). Rating Super-Resolution Microscopy Images With Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12186