Learned Exposure Selection for High Dynamic Range Image Synthesis

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dc.contributor.advisorBrown, Michael
dc.contributor.advisorBrubaker, Marcus
dc.contributor.authorSegal, Shane Maxwell
dc.date.accessioned2021-03-08T17:30:04Z
dc.date.available2021-03-08T17:30:04Z
dc.date.copyright2021-01
dc.date.issued2021-03-08
dc.date.updated2021-03-08T17:30:03Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractHigh dynamic range (HDR) imaging is a photographic technique that captures a greater range of luminance than standard imaging techniques. Traditionally accomplished by specialized sensors, HDR images are regularly created through the fusion of multiple low dynamic range (LDR) images that can now be captured by smartphones or other consumer grade hardware. Three or more images are traditionally required to generate a well-exposed HDR image. This thesis presents a novel system for the fast synthesis of HDR images by means of exposure fusion with only two images required. Experiments show that a sufficiently trained neural network can predict a suitable exposure value for the next image to be captured, when given an initial image as input. With these images fed into the exposure fusion algorithm, a high-quality HDR image can be quickly generated.
dc.identifier.urihttp://hdl.handle.net/10315/38241
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.subject.keywordscomputer vision
dc.subject.keywordsmachine learning
dc.subject.keywordscomputational photography
dc.subject.keywordsdeep learning
dc.subject.keywordsexposure fusion
dc.subject.keywordsartificial intelligence
dc.titleLearned Exposure Selection for High Dynamic Range Image Synthesis
dc.typeElectronic Thesis or Dissertation

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