TUTCRIS

TY - JOUR

T1 - Accelerated Shearlet-Domain Light Field Reconstruction

AU - Vagharshakyan, Suren

AU - Bregovic, Robert

AU - Gotchev, Atanas

PY - 2017

Y1 - 2017

N2 - We consider the problem of reconstructing densely sampled light field (DSLF) from sparse camera views. In our previous work, the DSLF has been reconstructed by processing epipolar-plane images (EPI) employing sparse regularization in shearlet transform domain. With the aim to avoid redundant processing and reduce the overall reconstruction time, in this article we propose algorithm modifications in three directions. First, we modify the basic algorithm by offering a faster and more stable iterative procedure. Second, we elaborate on the proper use of color redundancy by studying the effect of reconstruction of an average intensity channel and its use as a guiding mode for colorizing the three color channels. Third, we explore similarities between EPIs by their grouping and joint processing or by effective decorrelation to get an initial estimate for the basic iterative procedure. We are specifically interested in GPU-based computations allowing an efficient implementation of the shearlet transform. We quantify our three main approaches to accelerated processing over a wide collection of horizontal- as well as full-parallax datasets.

AB - We consider the problem of reconstructing densely sampled light field (DSLF) from sparse camera views. In our previous work, the DSLF has been reconstructed by processing epipolar-plane images (EPI) employing sparse regularization in shearlet transform domain. With the aim to avoid redundant processing and reduce the overall reconstruction time, in this article we propose algorithm modifications in three directions. First, we modify the basic algorithm by offering a faster and more stable iterative procedure. Second, we elaborate on the proper use of color redundancy by studying the effect of reconstruction of an average intensity channel and its use as a guiding mode for colorizing the three color channels. Third, we explore similarities between EPIs by their grouping and joint processing or by effective decorrelation to get an initial estimate for the basic iterative procedure. We are specifically interested in GPU-based computations allowing an efficient implementation of the shearlet transform. We quantify our three main approaches to accelerated processing over a wide collection of horizontal- as well as full-parallax datasets.

U2 - 10.1109/JSTSP.2017.2738617

DO - 10.1109/JSTSP.2017.2738617

M3 - Article

VL - 11

JO - IEEE Journal of Selected Topics in Signal Processing

JF - IEEE Journal of Selected Topics in Signal Processing

SN - 1932-4553

IS - 7

ER -