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Coding Depth through Mask Structure
Author(s) -
Fortunato Horacio E.,
Oliveira Manuel M.
Publication year - 2012
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2012.03025.x
Subject(s) - deconvolution , encode , coding (social sciences) , algorithm , computer science , component (thermodynamics) , convolution (computer science) , dirac delta function , computer vision , artificial intelligence , mathematics , physics , mathematical analysis , artificial neural network , biochemistry , chemistry , statistics , gene , thermodynamics
We present a coded‐aperture method based on a family of masks obtained as the convolution of one “hole” with a structural component consisting of an arrangement of Dirac delta functions. We call the arrangement of delta functions the structural component of the mask, and use it to efficiently encode scene distance information. We illustrate the potential of our approach by analyzing a family of masks defined by a circular hole component and a structural component consisting of a linear combination of three Dirac deltas. We show that the structural component transitions from well conditioned to ill conditioned as the relative weight of the central peak varies with respect to the lateral ones. For the well‐conditioned structural components, deconvolution is efficiently performed by inverse filtering, allowing for fast estimation of scene depth. We demonstrate the effectiveness of our approach by constructing a mask for distance coding and using it to recover pairs of distance maps and structurally‐deconvolved images from single photographs. For this application, we obtain significant speedup, and extended range and depth resolution compared to previous techniques.