Reconfigurable Snapshot HDR Imaging Using Coded Masks and Inception Network

dc.contributor.authorAlghamdi, Mashealen_US
dc.contributor.authorFu, Qiangen_US
dc.contributor.authorThabet, Alien_US
dc.contributor.authorHeidrich, Wolfgang en_US
dc.contributor.editorSchulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michaelen_US
dc.date.accessioned2019-09-29T06:45:50Z
dc.date.available2019-09-29T06:45:50Z
dc.date.issued2019
dc.description.abstractHigh Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this paper we propose a joint design for snapshot HDR imaging by devising a spatially-varying modulation mask in the hardware as well as building an inception network to reconstruct the HDR image. We achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible yet robust way to modulating per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparison results show that our method outperforms state of the art in terms of visual perception quality.en_US
dc.description.sectionheadersImaging
dc.description.seriesinformationVision, Modeling and Visualization
dc.identifier.doi10.2312/vmv.20191316
dc.identifier.isbn978-3-03868-098-7
dc.identifier.pages37-45
dc.identifier.urihttps://doi.org/10.2312/vmv.20191316
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20191316
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectComputational photography
dc.titleReconfigurable Snapshot HDR Imaging Using Coded Masks and Inception Networken_US
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