Leveraging Blur Information for Plenoptic Camera Calibration

• Published in: International journal of computer vision Vol. 130; no. 7; pp. 1655 - 1677
• Main Authors: Labussière, Mathieu, Teulière, Céline, Bernardin, Frédéric, Ait-Aider, Omar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2022; Springer; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Artificial Intelligence; Calibration; Cameras; Circles (geometry); Computer Imaging; Computer Science; Computer Vision and Pattern Recognition; Configurations; Cost function; Depth of field; Image Processing and Computer Vision; Image reconstruction; Microlenses; Optimization; Pattern Recognition; Pattern Recognition and Graphics; Point spread functions; Projection model; Special Issue on 3D Computer Vision; Vision; Plenoptic camera; Calibration; Blur circle; Relative blur; Multi-focus
• DOI: 10.1007/s11263-022-01582-z
• ISSN: 0920-5691

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.