Dappled photography: Mask enhanced cameras for heterodyned light fields and coded aperture refocusing

Ashok Veeraraghavan*, Ramesh Raskar, Amit Agrawal, Ankit Mohan, Jack Tumblin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

111 Scopus citations

Abstract

We describe a theoretical framework for reversibly modulating 4D light fields using an attenuating mask in the optical path of a lens based camera. Based on this framework, we present a novel design to reconstruct the 4D light field from a 2D camera image without any additional refractive elements as required by previous light field cameras. The patterned mask attenuates light rays inside the camera instead of bending them, and the attenuation recoverably encodes the rays on the 2D sensor. Our mask-equipped camera focuses just as a traditional camera to capture conventional 2D photos at full sensor resolution, but the raw pixel values also hold a modulated 4D light field. The light field can be recovered by rearranging the tiles of the 2D Fourier transform of sensor values into 4D planes, and computing the inverse Fourier transform. In addition, one can also recover the full resolution image information for the in-focus parts of the scene. We also show how a broadband mask placed at the lens enables us to compute refocused images at full sensor resolution for layered Lambertian scenes. This partial encoding of 4D ray-space data enables editing of image contents by depth, yet does not require computational recovery of the complete 4D light field.

Original languageEnglish (US)
Title of host publicationProceedings of ACM SIGGRAPH 2007
PublisherAssociation for Computing Machinery
DOIs
StatePublished - Jan 1 2007

ASJC Scopus subject areas

  • Computer Science(all)

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    Veeraraghavan, A., Raskar, R., Agrawal, A., Mohan, A., & Tumblin, J. (2007). Dappled photography: Mask enhanced cameras for heterodyned light fields and coded aperture refocusing. In Proceedings of ACM SIGGRAPH 2007 [69] Association for Computing Machinery. https://doi.org/10.1145/1275808.1276463