Light field

About: Light field is a research topic. Over the lifetime, 5357 publications have been published within this topic receiving 87424 citations.

Self-action of continuous laser radiation and Pearcey diffraction in a water suspension with light-absorbing particles

Abstract: Water suspension of light-absorbing nano-sized particles is an example of a medium in which non-linear effects are present at moderate light intensities favorable for optical treatment of organic and biological objects. We study experimentally the phenomena emerging in a thin layer of such a medium under the action of inhomogeneous light field formed due to the Pearcey diffraction pattern near a microlens focus. In this high-gradient field, the light energy absorbed by the particles induces inhomogeneous distribution of the medium refraction index, which results in observable self-diffraction of the incident light, here being strongly sensitive to the medium position with respect to the focus. This technique, based on the complex spatial structure of both the incident and the diffracted fields, can be employed for the detection and measurement of weak non-linearities.

Self-consistent characterization of light statistics

Abstract: We demonstrate the possibility of a self-consistent characterization of the photon-number statistics of a light field by using photoemissive detectors with internal gain simply endowed with linear input/output responses. The method can be applied to both microscopic and mesoscopic photon-number regimes. The detectors must operate in the linear range without the need for photon-counting capabilities.

Depth from a Light Field Image with Learning-Based Matching Costs

Abstract: One of the core applications of light field imaging is depth estimation. To acquire a depth map, existing approaches apply a single photo-consistency measure to an entire light field. However, this is not an optimal choice because of the non-uniform light field degradations produced by limitations in the hardware design. In this paper, we introduce a pipeline that automatically determines the best configuration for photo-consistency measure, which leads to the most reliable depth label from the light field. We analyzed the practical factors affecting degradation in lenslet light field cameras, and designed a learning based framework that can retrieve the best cost measure and optimal depth label. To enhance the reliability of our method, we augmented an existing light field benchmark to simulate realistic source dependent noise, aberrations, and vignetting artifacts. The augmented dataset was used for the training and validation of the proposed approach. Our method was competitive with several state-of-the-art methods for the benchmark and real-world light field datasets.

Singular beam microscopy

Abstract: Optical singularities are localized regions in a light field where one or more of the field parameters, such as phase or polarization, become singular with associated zero intensity. Singular beam microscopy exploits the fact that the strong variations of the optical field around the singularities are highly sensitive to changes in their neighborhood. As a consequence, analysis of the light field scattered from the object during a scanning process can yield useful information about the object features. We present a theoretical background, numerical simulations, and experimental results. Preliminary experiments have demonstrated a sensitivity of 20 nm in the position and size of simple objects, with theoretically estimated 1 nm capability under the assumption of a reasonable and conservative 30 dB signal to noise ratio.

Super Resolution of Light Field Images Using Linear Subspace Projection of Patch-Volumes

Abstract: Light field imaging has emerged as a very promising technology in the field of computational photography. Cameras are becoming commercially available for capturing real-world light fields. However, capturing high spatial resolution light fields remains technologically challenging, and the images rendered from real light fields have today a significantly lower spatial resolution compared to traditional two-dimensional (2-D) cameras. This paper describes an example-based super-resolution algorithm for light fields, which allows the increase of the spatial resolution of the different views in a consistent manner across all subaperture images of the light field. The algorithm learns linear projections between subspaces of reduced dimension in which reside patch-volumes extracted from the light field. The method is extended to cope with angular super-resolution, where 2-D patches of intermediate subaperture images are approximated from neighboring subaperture images using multivariate ridge regression. Experimental results show significant quality improvement when compared to state-of-the-art single-image super-resolution methods applied on each view separately, as well as when compared to a recent light field super-resolution techniques based on deep learning.