Paper Info
Title
Iest: Interpolation-Enhanced Shearlet Transform For Light Field Reconstruction Using Adaptive Separable Convolution
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 horizontal-parallax evaluation SSLF datasets of different real-world scenes with moderate and large disparity ranges.
Year
DOI
Venue
2019
10.23919/EUSIPCO.2019.8903168
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
Keywords
Field
DocType
Light Field Reconstruction, Parallax View Generation, Adaptive Separable Convolution, Shearlet Transform, Interpolation-Enhanced Shearlet Transform
Computer vision,Parallax,Convolution,Computer science,Interpolation,Separable space,Light field,Reconstruction algorithm,Motion interpolation,Pixel,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yuan Gao101.01
Reinhard Koch22038170.17
Robert Bregovic314818.84
Atanas P. Gotchev411.70