Abstract
Light-field cameras play a vital role for rich 3D 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, 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 consistency across viewpoints at the expense of high computational loads. The framework presented herein advances prior outcomes thanks to its novel micro image scale-space analysis for generic camera calibration independent of the lens specifications and its parallax-invariant, cost-effective viewpoint color equalization from optimal transport theory. Artifacts from the sensor and micro lens grid are compensated 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. Results from a Wasserstein distance further show that our color transfer outdoes the existing transport methods. Our algorithms are released under an open-source license, offer cross-platform compatibility with few dependencies and different user interfaces. 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.
Highlights
T HERE is a growing body of literature in the fields of experimental photography [1], [2], medical imaging [3], [4], [5], [6] and machine learning [7] recognizing capabilities offered by light-fields
The probably most influential light-field model in computer graphics was devised by Levoy and Hanrahan [8] who described a light-field to be a collection of ray vectors piercing through two planes stacked behind one another
Equipped with generic calibration and novel rendering routines, PLENOPTICAM may build the foundation for future work on light-field image algorithms
Summary
T HERE is a growing body of literature in the fields of experimental photography [1], [2], medical imaging [3], [4], [5], [6] and machine learning [7] recognizing capabilities offered by light-fields
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