Blur Aware Calibration of Multi-Focus Plenoptic Camera

Labussière, Mathieu; Teulière, Céline; Bernardin, Frédéric; Ait-Aider, Omar

doi:10.1109/cvpr42600.2020.00262

Cited by 7 publications

(19 citation statements)

References 28 publications

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“…This paper extends our previous work (Labussière et al, 2020). In addition to our former contributions, we present Fig.…”

Section: Contributionssupporting

confidence: 80%

Leveraging blur information for plenoptic camera calibration

Labussière,

Teulière,

Bernardin

et al. 2021

Preprint

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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 This work was supported by the AURA Region and the European Union (FEDER) through the MMII project of CPER 2015-2020 MMaSyF challenge.

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“…This paper extends our previous work (Labussière et al, 2020). In addition to our former contributions, we present Fig.…”

Section: Contributionssupporting

confidence: 80%

Leveraging blur information for plenoptic camera calibration

Labussière,

Teulière,

Bernardin

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This paper extends our previous work (Labussière et al 2020). In addition to our former contributions, we present here an ablation study of the camera parameters and add further comparisons with state-of-the-art calibration methods.…”

Section: Contributionssupporting

confidence: 58%

Leveraging Blur Information for Plenoptic Camera Calibration

et al. 2022

Self Cite

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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.

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“…The relationships among the blur circle radius R ðiÞ , the distance (d) between the MLA and the sensor, the distance (D) between the MLA and the main lens, and the focal lengths (f ðiÞ ) of the i'th type microlens are detailed in the literature. 16 R ðiÞ is expressed as 16…”

Section: Blur Circles Of Subimages In White Raw Imagesmentioning

confidence: 99%

“…Therefore, the virtual blur circle of the corner p corner is set as p r ¼ ðu; v; r ðiÞ ; 1Þ ⊤ . According to the literature, 16 the blur circle radius r ðiÞ of the corner in the checkerboard image is expressed as…”

Section: Blur Circles Of Corners In Checkerboard Imagesmentioning

confidence: 99%

“…In multifocus light field cameras, the microlenses with different focal lengths form the blur circles with different radii in the white raw image. Based on these characteristics, Labussière et al 16,17 introduced the geometric parameters of multifocus light field cameras into the calibration model through the blur circle radii of the image corners formed by the checkerboard corners, and realized the geometric parameter calibration of multifocal light field cameras. They also proposed a precalibration step using the blur circle radii of the subimages in white raw images to provide a robust initial estimate of geometric parameters.…”

Section: Introductionmentioning

confidence: 99%

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Improved blur circle detection method for geometric calibration of multifocus light field cameras

Chen¹,

Lu²,

Wang³

et al. 2022

Opt. Eng.

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Multifocus light field cameras are increasingly gaining attention in the field of computational imaging and machine vision due to their ability to capture spatial and angular information of light rays. Accurate calibration of the geometric parameters of multifocus light field cameras is the prerequisite for implementing computational imaging techniques, such as digital refocusing, depth reconstruction, and three-dimensional reconstruction. We propose an improved blur circle detection method for the calibration of multifocus light field cameras. The circular Hough transform-based circle detection method is used to obtain the subimage centers accurately in white raw images. On this basis, the subimages in the checkerboard images are classified, and the image corners are detected to determine the clusters of image corners corresponding to checkerboard corners. Then, the centers of plenoptic disc features are used as projection points of checkerboard corners on the light field image to calculate the camera pose. Levenberg-Marquardt method is further used to solve the calibration model. Finally, the calibration experiments of multifocus light field cameras are carried out to evaluate the improved blur circle detection method. Experimental results indicated that the improved blur circle detection method has higher calibration accuracy of multifocus light field camera than the blur circle detection method, while the mean reprojection error of the microlens centers and the image corners are 0.12 and 0.33 pixels, and the mean relative distance error between adjacent checkerboards is 2.53%.

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Blur Aware Calibration of Multi-Focus Plenoptic Camera

Cited by 7 publications

References 28 publications

Leveraging blur information for plenoptic camera calibration

Leveraging blur information for plenoptic camera calibration

Leveraging Blur Information for Plenoptic Camera Calibration

Improved blur circle detection method for geometric calibration of multifocus light field cameras

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