Lightfield Recovery from Its Focal Stack

被引:0
作者
F. Pérez
A. Pérez
M. Rodríguez
E. Magdaleno
机构
[1] University of La Laguna,Departamento de Estadística, Investigación Operativa y Computación
[2] University of La Laguna,Departamento de Física Fundamental, Experimental, Electrónica y Sistemas
来源
Journal of Mathematical Imaging and Vision | 2016年 / 56卷
关键词
Lightfield; Focal Stack; Plenoptic; Inverse problems; Regularization;
D O I
暂无
中图分类号
学科分类号
摘要
The Focal Stack Transform integrates a 4D lightfield over a set of appropriately chosen 2D planes. The result of such integration is an image focused on a determined depth in 3D space. The set of such images is the Focal Stack of the lightfield. This paper studies the existence of an inverse for this transform. Such inverse could be used to obtain a 4D lightfield from a set of images focused on several depths of the scene. In this paper, we show that this inversion cannot be obtained for a general lightfield and introduce a subset of lightfields where this inversion can be computed exactly. We examine the numerical properties of such inversion process for general lightfields and examine several regularization approaches to stabilize the transform. Experimental results are provided for focal stacks obtained from several plenoptic cameras. From a practical point of view, results show how this inversion procedure can be used to recover, compress, and denoise the original 4D lightfield.
引用
收藏
页码:573 / 590
页数:17
相关论文
共 33 条
[1]  
Levoy M(2006)Light fields and computational imaging Computer 39 46-55
[2]  
Lüke JP(2009)Near real-time estimation of super-resolved depth and all-in-focus images from a plenoptic camera using graphics processing units Int. J. Digit. Multimed. Broadcast. 2010 e942037-217
[3]  
Pérez Nava F(2015)Super-resolved Fourier-slice refocusing in plenoptic cameras J. Math. Imaging Vis. 52 200-105
[4]  
Marichal-Hernández JG(2015)A fast and memory-efficient Discrete Focal Stack Transform for plenoptic sensors Digit. Signal Process. 38 95-87
[5]  
Rodríguez-Ramos JM(2013)Old and new parameter choice rules for discrete ill-posed problems Numer. Algorithms 63 65-268
[6]  
Rosa F(1992)Nonlinear total variation based noise removal algorithms Phys. Nonlinear Phenom. 60 259-2356
[7]  
Pérez F(2010)Fast image recovery using variable splitting and constrained optimization IEEE Trans. Image Process. 19 2345-343
[8]  
Pérez A(2009)The split Bregman method for L1-regularized problems SIAM J. Imaging Sci. 2 323-592
[9]  
Rodríguez M(2009)A fast algorithm for edge-preserving variational multichannel image restoration SIAM J. Imaging Sci. 2 569-612
[10]  
Magdaleno E(2004)Image quality assessment: from error visibility to structural similarity IEEE Trans. Image Process. 13 600-218