  This paper presents a novel calibration algorithm for plenoptic cameras,
especially the multi-focus configuration, where several types of micro-lenses
are used, using raw images only. Current calibration methods rely on simplified
projection models, use features from reconstructed images, or require separated
calibrations for each type of micro-lens. In the multi-focus configuration, the
same part of a scene will demonstrate different amounts of blur according to
the micro-lens focal length. Usually, only micro-images with the smallest
amount of blur are used. In order to exploit all available data, we propose to
explicitly model the defocus blur in a new camera model with the help of our
newly introduced Blur Aware Plenoptic (BAP) feature. First, it is used in a
pre-calibration step that retrieves initial camera parameters, and second, to
express a new cost function to be minimized in our single optimization process.
Third, it is exploited to calibrate the relative blur between micro-images. It
links the geometric blur, i.e., the blur circle, to the physical blur, i.e.,
the point spread function. Finally, we use the resulting blur profile to
characterize the camera's depth of field. Quantitative evaluations in
controlled environment on real-world data demonstrate the effectiveness of our
calibrations.
