End-to-End Computational Lensless Imaging with Perceptual Loss

Ya Ti Chang Lee, Chung Hao Tien

Research output: Contribution to journalConference articlepeer-review

Abstract

Recently, computational lensless imaging had been making progress with the evolution of artificial neural networks. Nonetheless, generative models for image reconstruction inherit challenge due to its ill-posed nature. We proposed a deep neural network based lensless imaging system by optimizing perceptual loss exclusively to end-to-end reconstruct images conforming human preference.

Original languageEnglish
Pages (from-to)1153-1156
Number of pages4
JournalProceedings of the International Display Workshops
Volume29
StatePublished - 2022
Event29th International Display Workshops, IDW 2022 - Fukuoka, Japan
Duration: 14 Dec 202216 Dec 2022

Keywords

  • artificial neural network
  • coded aperture
  • Lensless imaging
  • perceptual loss

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