Leveraging blur information for plenoptic camera calibration

TL;DR

This paper introduces a new calibration method for multi-focus plenoptic cameras that explicitly models defocus blur using raw images, improving calibration accuracy by leveraging all available data and characterizing depth of field.

Contribution

The paper proposes a novel camera model with a Blur Aware Plenoptic feature for calibration, enabling the use of all micro-image data and linking geometric and physical blur.

Findings

Effective calibration on real-world data

Improved depth of field characterization

Utilizes all micro-image data for calibration

Abstract

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.