Principles of Light Field Imaging: Briefly revisiting 25 years of research

Ihrke, Ivo; Tamayo, John Fernando Restrepo; Mignard-Debise, Loïs

doi:10.1109/msp.2016.2582220

Cited by 138 publications

(58 citation statements)

References 41 publications

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“…The rapid development of computational photography has brought great advances and changes to immersive 3D applications. As a computational photography technique, a light field (LF) represents all the light information in a scene, and thus provides a more immersive experience for audiences [1,2]. Because conventional imaging technologies project a 3D scene onto a 2D plane, there is a loss of stereoscopic sensation.…”

Section: Introductionmentioning

confidence: 99%

Light-field compression using a pair of steps and depth estimation

An¹,

Cao²,

Liu³

et al. 2019

Opt. Express

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Advanced handheld plenoptic cameras are being rapidly developed to capture information about light fields (LFs) from the 3D world. Rich LF data can be used to develop dense sub-aperture images (SAIs) that can provide a more immersive experience for users. Unlike conventional 2D images, 4D SAIs contain both the positional and directional information of light rays; the practical applications of handheld plenoptic cameras are limited by the huge volume of data required to capture this information. Therefore, an efficient LF compression method is vital for further application of the cameras. To this end, the pair of steps and depth estimation (PoS&DE) method is proposed in this paper, and the multiview video and depth (MVD) coding structure is used to relieve the LF coding burden. More specifically, a precise depth-estimation approach is presented for SAIs based on the cost function, and an SAI-guided depth optimization algorithm is designed to refine the initial depth map based on pixel variation tendency. Meanwhile, to reduce running time, intermediate SAI synthesis quality and coding bitrates, including the key SAIs selected and cost-computation steps, are set via extensive statistical experiments. In this way, only a limited number of optimally selected SAIs and their corresponding depth maps must be encoded. The experimental results demonstrate that our proposed LF compression solution using PoS&DE can obtain a satisfied coding performance.

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Section: Introductionmentioning

confidence: 99%

Light-field compression using a pair of steps and depth estimation

An¹,

Cao²,

Liu³

et al. 2019

Opt. Express

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“…Light-Field imaging has been mainly studied for applications in the visible light such as in photography, in object recognition and in microscopy 1,2,3 . Different kinds of Light-Field cameras have been developed 4,5,6 . In this paper, we focus on plenoptic cameras made of the following three optical elements: a main lens, a microlens array, and a detector.…”

Section: Introductionmentioning

confidence: 99%

Comparison of reconstruction approaches for plenoptic imaging systems

Herzog

Dovillaire

Granier

et al. 2018

Unconventional Optical Imaging

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Plenoptic cameras provide single-shot 3D imaging capabilities, based on the acquisition of the Light-Field, which corresponds to a spatial and directional sampling of all the rays of a scene reaching a detector. Specific algorithms applied on raw Light-Field data allow for the reconstruction of an object at different depths of the scene.Two different plenoptic imaging geometries have been reported, associated with two reconstruction algorithms: the traditional or unfocused plenoptic camera, also known as plenoptic camera 1.0, and the focused plenoptic camera, also called plenoptic camera 2.0. Both systems use the same optical elements, but placed at different locations: a main lens, a microlens array and a detector. These plenoptic systems have been presented as independent. Here we show the continuity between them, by simply moving the position of an object. We also compare the two reconstruction methods. We theoretically show that the two algorithms are intrinsically based on the same principle and could be applied to any LightField data. However, the resulting images resolution and quality depend on the chosen algorithm.

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“…The 2D images observed by humans at each angle are processed versions of the higher-dimensional light information in the scene. In terms of signal processing, we are presented with a high-dimensional sampling problem with nonuniform and nonlinear sample spacing and high-dimensional spatiodirectionally varying sampling kernels [2]. This high dimensional space is parametrized by multiple definitions.…”

Section: Introductionmentioning

confidence: 99%

Steered Mixture-of-Experts Approximation of Spherical Image Data

Verhack

Madhu

Wallendael

et al. 2018

2018 26th European Signal Processing Conference (EUSIPCO)

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Steered Mixture-of-Experts (SMoE) is a novel framework for approximating multidimensional image modalities. Our goal is to provide full Six Degrees-of-Freedom capabilities for camera captured content. Previous research concerned only limited translational movement for which the 4D light field representation is sufficient. However, our goal is to arrive at a representation that allows for unlimited translational-rotational freedom, i.e. our goal is to approximate the full 5D plenoptic function. Until now, SMoE was only applied on Euclidean spaces. However, the plenoptic function contains two spherical coordinate dimensions. In this paper, we propose a methodology to extend the SMoE framework to spherical dimensions. Furthermore, we propose a method to reduce the parameter space to the same two dimensional Euclidean space as for planar 2D images by using a projection of the covariance matrices onto tangent spaces perpendicular to the unit sphere. Finally, we propose a novel training technique for spherical dimensions based on these observations. Experiments performed on omnidirectional 360°images show that the introduction of the dimensionalityreduction projection step results in very low quality loss.

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Principles of Light Field Imaging: Briefly revisiting 25 years of research

Cited by 138 publications

References 41 publications

Light-field compression using a pair of steps and depth estimation

Light-field compression using a pair of steps and depth estimation

Comparison of reconstruction approaches for plenoptic imaging systems

Steered Mixture-of-Experts Approximation of Spherical Image Data

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