Optical three-dimensional refocusing from elemental images based on a sifting property of the periodic δ-function array in integral-imaging

Abstract

We propose a novel approach to optically refocus three-dimensional (3-D) objects on their real depth from the captured elemental image array (EIA) by using a sifting property of the periodic δ-function array (PDFA) in integral-imaging. By convolving the PDFAs whose spatial periods correspond to each object's depth with the sub-image array (SIA) transformed from the EIA, a set of spatially filtered-SIAs (SF-SIAs) for each object's depth can be extracted. These SF-SIAs are then inverse-transformed into the corresponding versions of the EIAs, and from these, 3-D objects with their own perspectives can be reconstructed to be refocused on their depth in the space. The feasibility of the proposed method has been confirmed through optical experiments as well as ray-optical analysis.

A fast and memory-efficient Discrete Focal Stack Transform for plenoptic sensors

“…The Fourier Slice technique uses the Kaiser Bessel kernel while the Discrete Focal Stack uses the Dirichlet kernel. The refocusing problem has also been studied in the integral imaging field [12,13]. Note that although a plenoptic camera captures O(n 2 m 2 ) pixels they are decomposed into O(n 2 ) pixels providing spatial information and O(m 2 ) providing directional information, so the resolution of the refocused image is O(n 2 ) and several algorithms have been developed to increase the spatial resolution of the photographs in the Focal Stack using super-resolution methods.…”

Fast computational integral imaging reconstruction by combined use of spatial filtering and rearrangement of elemental image pixels

“…Considering that the rays are emitted from the point object to pass through the optical center of a pickup lens, the geometrical relation between a point object, its corresponding imaging points on the elemental image plane can be given by [15] x…”

Computational Integral Imaging Reconstruction of a Partially Occluded Three-Dimensional Object Using an Image Inpainting Technique

“…Computational integral imaging (CII) is able to capture and reconstruct partially occluded 3D objects for 3D visualization and recognition [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. CII consists of the pickup process and the subsequent computational integral imaging reconstruction (CIIR) process.…”