Deep neural networks for single-pixel compressive video reconstruction - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Deep neural networks for single-pixel compressive video reconstruction

Antonio Lorente Mur
  • Fonction : Auteur
  • PersonId : 1042728
Nicolas Ducros

Résumé

Single-pixel imaging is a paradigm that enables the capture of an image from a single point detector using a spatial light modulator. This approach is particularly interesting for optical setups where pixelated arrays of detectors are either too expensive or too cumbersome (e.g., multispectral, infrared imaging). It acquires the inner product between the image of the scene and a set of user-defined patterns that are sequentially uploaded onto the spatial light modulator. Compressed data acquisition reduces the acquisition time, although it leads to an ill-posed reconstruction problem, which is very challenging for real-time applications. Recently, neural networks have emerged as competitive alternatives to traditional reconstruction methods. Neural networks are parametric models that are trained by exploiting large datasets. Their noniterative nature allows for fast reconstructions, which opens the door to real-time image reconstruction from compressed acquisition. In this study, we evaluate the different networks for static and dynamic imaging. In particular, we introduce a recurrent neural network that is designed to exploit the spatiotemporal redundancy in videos via a memory state. We validate our algorithms on simulated data from the UCF-101 dataset, with a resolution of 128×128 pixels and a compression ratio of 98%. We also show experimentally that we can resolve small spectral differences in the spectrum of human skin measured in vivo.
Fichier principal
Vignette du fichier
2020_SPIE__Photonics_Europe_Deep_neural_networks_for_single_pixel_compressive_video_reconstruction(1).pdf (15.28 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02547800 , version 1 (20-04-2020)

Identifiants

Citer

Antonio Lorente Mur, Bruno Montcel, Françoise Peyrin, Nicolas Ducros. Deep neural networks for single-pixel compressive video reconstruction. Unconventional Optical Imaging II, Apr 2020, Online Only, France. pp.27, ⟨10.1117/12.2553326⟩. ⟨hal-02547800⟩
241 Consultations
58 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More