Post-capture focusing using regression forest

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dc.contributor.author Kanojia, Gagan
dc.contributor.author Raman, Shanmuganathan
dc.date.accessioned 2017-04-15T19:40:25Z
dc.date.available 2017-04-15T19:40:25Z
dc.date.issued 2017-06
dc.identifier.citation Kanojia, Gagan and Raman, Shanmuganathan, “Post-capture focusing using regression forest”, IEEE Signal Processing Letters, DOI: 10.1109/LSP.2017.2690621, vol. 24, no. 6, pp. 751-755, Jun. 2017.
dc.identifier.issn 1070-9908
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/2842
dc.identifier.uri https://doi.org/10.1109/LSP.2017.2690621
dc.description.abstract A photograph of the same scene can look different when captured using different camera settings. In this paper, we propose a novel technique to obtain the complete post-capture control over the focus and aperture settings of a traditional camera by acquiring small number of images. In this work, we tackle the problem of deciding which focus-aperture (F-A) combinations should be used to capture the input images, by solving it as a center selection problem. The images captured with the selected settings are then used to reconstruct the images for all possible F-A settings of a traditional camera. For the reconstruction of the images, we have used random regression forest. We show that the proposed approach provides an effective alternative for post-capture control in photography. en_US
dc.description.statementofresponsibility by Gagan Kanojia and Shanmuganathan Raman
dc.format.extent Vol. 24, no. 6, pp. 751-755
dc.language.iso en_US en_US
dc.publisher IEEE en_US
dc.subject Cameras en_US
dc.subject Apertures en_US
dc.subject Image reconstruction en_US
dc.subject Training en_US
dc.subject Vegetation en_US
dc.subject Lenses en_US
dc.subject Principal component analysis en_US
dc.title Post-capture focusing using regression forest en_US
dc.type Article en_US
dc.relation.journal IEEE Signal Processing Letters


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