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Compressive Dual Photography
Author(s) -
Sen Pradeep,
Darabi Soheil
Publication year - 2009
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.1467-8659.2009.01401.x
Subject(s) - computer science , leverage (statistics) , computational photography , photography , computer vision , dual (grammatical number) , artificial intelligence , computer graphics (images) , domain (mathematical analysis) , computer graphics , compressed sensing , signal (programming language) , graphics , image (mathematics) , image processing , mathematics , art , mathematical analysis , visual arts , literature , programming language
The accurate measurement of the light transport characteristics of a complex scene is an important goal in computer graphics and has applications in relighting and dual photography. However, since the light transport data sets are typically very large, much of the previous research has focused on adaptive algorithms that capture them efficiently. In this work, we propose a novel, non‐adaptive algorithm that takes advantage of the compressibility of the light transport signal in a transform domain to capture it with less acquisitions than with standard approaches. To do this, we leverage recent work in the area of compressed sensing, where a signal is reconstructed from a few samples assuming that it is sparse in a transform domain. We demonstrate our approach by performing dual photography and relighting by using a much smaller number of acquisitions than would normally be needed. Because our algorithm is not adaptive, it is also simpler to implement than many of the current approaches.