Tampere University of Technology

TUTCRIS Research Portal

IEST: Interpolation-Enhanced Shearlet Transform for Light Field Reconstruction Using Adaptive Separable Convolution

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 27th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Number of pages5
ISBN (Electronic)978-9-0827-9703-9
ISBN (Print)978-1-5386-7300-3
DOIs
Publication statusPublished - Sep 2019
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Period1/01/00 → …

Abstract

The performance of a light field reconstruction algorithm is typically affected by the disparity range of the input Sparsely-Sampled Light Field (SSLF). This paper finds that (i) one of the state-of-the-art video frame interpolation methods, i.e. adaptive Separable Convolution (SepConv), is especially effective for the light field reconstruction on a SSLF with a small disparity range (<10 pixels); (ii) one of the state-of-the-art light field reconstruction methods, i.e. Shearlet Transformation (ST), is especially effective in reconstructing a light field from a SSLF with a moderate disparity range (10-20 pixels) or a large disparity range (> 20 pixels). Therefore, to make full use of both methods to solve the challenging light field reconstruction problem on SSLFs with moderate and large disparity ranges, a novel method, referred to as Interpolation-Enhanced Shearlet Transform (IEST), is proposed by incorporating these two approaches in a coarse-to-fine manner. Specifically, ST is employed to give a coarse estimation for the target light field, which is then refined by SepConv to improve the reconstruction quality of parallax views involving small disparity ranges. Experimental results show that IEST outperforms the other state-of-the-art light field reconstruction methods on nine challenging horizontalparallax evaluation SSLF datasets of different real-world scenes with moderate and large disparity ranges.

Keywords

  • Light Field Reconstruction, Parallax View Generation, Adaptive Separable Convolution, Shearlet Transform, Interpolation-Enhanced Shearlet Transform

Publication forum classification

Field of science, Statistics Finland