Compressive Dual Photography.

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Bibliographic Details
Title:	Compressive Dual Photography.
Authors:	Sen, Pradeep¹, Darabi, Soheil¹
Source:	Computer Graphics Forum. Apr2009, Vol. 28 Issue 2, p609-618. 10p. 3 Color Photographs, 4 Black and White Photographs, 1 Chart.
Subjects:	Digital image processing software, Digitization, Computer graphics research, Computer graphics software, Digital photography software, Algorithms
Abstract:	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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	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. [ABSTRACT FROM AUTHOR]
ISSN:	01677055
DOI:	10.1111/j.1467-8659.2009.01401.x