Abstract
Light-field cameras play a vital role for rich 3-D information retrieval in narrow range depth sensing applications. The key obstacle in composing light-fields from exposures taken by a plenoptic camera is to computationally calibrate, re-align and rearrange four-dimensional image data. Several attempts have been proposed to enhance the overall image quality by tailoring pipelines dedicated to particular plenoptic cameras and improving the color consistency across viewpoints at the expense of high computational loads. The framework presented herein advances prior outcomes thanks to its cost-effective color equalization from parallax-invariant probability distribution transfers and a novel micro image scale-space analysis for generic camera calibration independent of the lens specifications. Our framework compensates for artifacts from the sensor and micro lens grid in an innovative way to enable superior quality in sub-aperture image extraction, computational refocusing and Scheimpflug rendering with sub-sampling capabilities. Benchmark comparisons using established image metrics suggest that our proposed pipeline outperforms state-of-the-art tool chains in the majority of cases. The algorithms described in this paper are released under an open-source license, offer cross-platform compatibility with few dependencies and a graphical user interface. This makes the reproduction of results and experimentation with plenoptic camera technology convenient for peer researchers, developers, photographers, data scientists and others working in this field.Citation
Hahne, C. and Aggoun, A. (2021) PlenoptiCam v1.0: A light-field imaging framework. IEEE Transactions on Image Processing, 30, pp.6757-6771.Publisher
IEEEJournal
IEEE Transactions on Image ProcessingAdditional Links
https://ieeexplore.ieee.org/document/9490522Type
Journal articleLanguage
enDescription
This is an accepted manuscript of an article published by IEEE in IEEE Transactions on Image Processing on 19/07/2021. Available online: https://doi.org/10.1109/TIP.2021.3095671 The accepted version of the publication may differ from the final published version.ISSN
1057-7149ae974a485f413a2113503eed53cd6c53
10.1109/TIP.2021.3095671
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/