no code implementations • 20 Mar 2020 • Yuan Gao, Robert Bregovic, Atanas Gotchev
Specifically, CycleST is composed of an encoder-decoder network and a residual learning strategy that restore the shearlet coefficients of densely-sampled EPIs using EPI reconstruction and cycle consistency losses.
Signal Processing Multimedia Image and Video Processing
no code implementations • 19 Mar 2020 • Yuan Gao, Robert Bregovic, Reinhard Koch, Atanas Gotchev
Specifically, for an input sparsely-sampled EPI, DRST employs a deep fully Convolutional Neural Network (CNN) to predict the residuals of the shearlet coefficients in shearlet domain in order to reconstruct a densely-sampled EPI in image domain.
1 code implementation • 29 Sep 2015 • Suren Vagharshakyan, Robert Bregovic, Atanas Gotchev
In this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras.
