Search Results for author: Atanas Gotchev

Found 7 papers, 1 papers with code

Optical modelling of accommodative light field display system and prediction of human eye responses

no code implementations2 Apr 2022 Yuta Miyanishi, Erdem Sahin, Atanas Gotchev

With the model, we simulated the retinal point spread function (PSF) of a point rendered by an LF display at various depths to characterise the retinal image quality.

Bi-directional Loop Closure for Visual SLAM

no code implementations1 Apr 2022 Ihtisham Ali, Sari Peltonen, Atanas Gotchev

As a result, most of the methods fail in the absence of a significantly similar overlap of perspectives.

Loop Closure Detection Visual Navigation

Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency

no code implementations20 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

DRST: Deep Residual Shearlet Transform for Densely Sampled Light Field Reconstruction

no code implementations19 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.

Learning Wavefront Coding for Extended Depth of Field Imaging

no code implementations31 Dec 2019 Ugur Akpinar, Erdem Sahin, Monjurul Meem, Rajesh Menon, Atanas Gotchev

Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information.

Deblurring

Light Field Reconstruction Using Shearlet Transform

1 code implementation29 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.

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