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Computational Photography

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Freeman, W., & Schölkopf, B. (2008). Computational Photography. Talk presented at NIPS 2008 Workshop: Computational Photography. Whistler, BC, Canada. 2008-12-12.


Cite as: http://hdl.handle.net/21.11116/0000-0003-A0C8-6
Abstract
Computation will change photography. The sensor no longer has to record the final image, but only data that can lead to the final image. Computation can solve longstanding photographic problems (e.g., deblurring) and well as open the door for radical new designs and capabilities for image capture, processing, and viewing (e.g., lightfield cameras). Many of these possibilities offer great machine learning problems, and much of the progress in computational photography will rely on solutions to these challenging machine learning problems. We have gathered five leading researchers in this new field to describe their work at the intersection of photography and machine learning.