Full-resolution light- field single-shot acquisition with spatial encoding

2011 ◽  
Author(s):  
Ryoichi Horisaki ◽  
Jun Tanida
Keyword(s):  
Light Field ◽  
Single Shot ◽  
Spatial Encoding ◽  
Full Resolution

A Fast View Synthesis Implementation Method for Light Field Applications

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Wei Gao ◽  
Linjie Zhou ◽  
Lvfang Tao
Keyword(s):  
Neural Networks ◽  
Light Field ◽  
Three Dimensional ◽  
Super Resolution ◽  
View Synthesis ◽  
Compact Representation ◽  
Light Weight ◽  
Computationally Efficient ◽  
Full Resolution ◽  
Time Systems

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

scholarly journals Vari-Focal Light Field Camera for Extended Depth of Field

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1453
Author(s):  
Hyun Myung Kim ◽  
Min Seok Kim ◽  
Sehui Chang ◽  
Jiseong Jeong ◽  
Hae-Gon Jeon ◽  
...  
Keyword(s):  
Light Field ◽  
High Reliability ◽  
Focal Length ◽  
Outdoor Environment ◽  
Depth Of Field ◽  
Depth Information ◽  
Single Shot ◽  
Depth Sensing ◽  
Depth Measurement ◽  
Sensing Applications

The light field camera provides a robust way to capture both spatial and angular information within a single shot. One of its important applications is in 3D depth sensing, which can extract depth information from the acquired scene. However, conventional light field cameras suffer from shallow depth of field (DoF). Here, a vari-focal light field camera (VF-LFC) with an extended DoF is newly proposed for mid-range 3D depth sensing applications. As a main lens of the system, a vari-focal lens with four different focal lengths is adopted to extend the DoF up to ~15 m. The focal length of the micro-lens array (MLA) is optimized by considering the DoF both in the image plane and in the object plane for each focal length. By dividing measurement regions with each focal length, depth estimation with high reliability is available within the entire DoF. The proposed VF-LFC is evaluated by the disparity data extracted from images with different distances. Moreover, the depth measurement in an outdoor environment demonstrates that our VF-LFC could be applied in various fields such as delivery robots, autonomous vehicles, and remote sensing drones.

scholarly journals Robust Depth Estimation for Light Field Microscopy

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 500 ◽  
Author(s):  
Luca Palmieri ◽  
Gabriele Scrofani ◽  
Nicolò Incardona ◽  
Genaro Saavedra ◽  
Manuel Martínez-Corral ◽  
...  
Keyword(s):  
Light Field ◽  
Three Dimensional ◽  
Depth Map ◽  
Depth Estimation ◽  
Fourier Integral ◽  
Single Shot ◽  
Natural Scene ◽  
Multi Scale ◽  
Depth Maps ◽  
New Applications

Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene. Many approaches are applicable, but most of them cannot achieve high accuracy because of the nature of such images: biological samples are usually poor in features and do not exhibit sharp colors like natural scene. Due to such conditions, standard approaches result in noisy depth maps. In this work, a robust approach is proposed where accurate depth maps can be produced exploiting the information recorded in the light field, in particular, images produced with Fourier integral Microscope. The proposed approach can be divided into three main parts. Initially, it creates two cost volumes using different focal cues, namely correspondences and defocus. Secondly, it applies filtering methods that exploit multi-scale and super-pixels cost aggregation to reduce noise and enhance the accuracy. Finally, it merges the two cost volumes and extracts a depth map through multi-label optimization.

Solar Concentrator Shape Characterisation Using Composite Light-Field Imaging

2015 ◽  
Author(s):  
Oliver Garbrecht ◽  
José Zapata ◽  
Reinhold Kneer
Keyword(s):  
Light Field ◽  
Single Point ◽  
Single Shot ◽  
Solar Concentrator ◽  
Original Design ◽  
Entire Surface ◽  
Manufacturing Defects ◽  
Wide Range ◽  
Light Field Imaging ◽  
Field Imaging

The shape of an installed solar concentrator (e.g. a heliostat) may differ from its original design due to manufacturing defects, structural/wind loads and thermal expansion. By measuring the shape of a solar concentrator, it is possible to account for the deviation in optical performance from its original design point. A method to measure concentrator shape needs to be fast, accurate, and not involve contact or interference with the reflective surface of the concentrator. State-of-the-art techniques include flux mapping, photogrammetry, and deflectometry using conventional cameras. This paper presents a study to characterise solar concentrator shapes using light-field imaging. Conventional cameras capture the light intensity of a point in a scene at a single point in the sensor, creating a two-dimensional image. A light field camera features multiple micro-lenses placed between the main lens and the sensor, providing many small images from slightly different angles in a single shot. This information is used to reconstruct the position of a light source in space. The advantage of this new technique to the ones mentioned above is that the light field camera is robust and self-contained, which allows easy-to-use application in heliostat fields. In this study, light-field camera measurements were performed with flat mirrors and a curved mirror under laboratory conditions. In order to resolve the surface of the mirror surfaces, several methods to impose contours of the mirror surface have been studied, including dirt, small water droplets, scattering of low-power laser light, and paper-marks. A wide range of camera-to-mirror distances between 43 cm and 5 m have been studied. Greater distances allow the capture of the entire surface, but decrease the precision of depth measurements. In order to obtain high precision measurements while being able to capture the entire surface, a compositing strategy has been developed, combining several light-field image measurements. The overall accuracy of the system was improved further by averaging measurements over several image frames. Subsequently, the reconstructed surface points have been fed to a ray-tracing algorithm realized in Matlab/Python. Results in this study are able to resolve the shape of small concentrators to sub-millimetre precision when taking pictures at a distance of 0.4 m.

scholarly journals Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras

2021 ◽  
Author(s):  
Luca Palmieri
Keyword(s):  
Light Field ◽  
Three Dimensional ◽  
Microlens Array ◽  
Depth Estimation ◽  
Calibration Method ◽  
Optimization Method ◽  
Disparity Estimation ◽  
Future Research ◽  
Single Shot ◽  
Estimation Algorithms

Microlens-array based plenoptic cameras capture the light field in a single shot, enabling new potential applications but also introducing additional challenges. A plenoptic image consists of thousand of microlens images. Estimating the disparity for each microlens allows to render conventional images, changing the perspective and the focal settings, and to reconstruct the three-dimensional geometry of the scene. The work includes a blur-aware calibration method to model plenoptic cameras, an optimization method to accurately select the best microlenses combination for disparity estimation, an overview of the different types of plenoptic cameras, an analysis of the disparity estimation algorithms, and a robust depth estimation approach for light field microscopy. The research led to the creation of a full framework for plenoptic cameras, which contains the implementation of the algorithms discussed in the work and datasets of both real and synthetic images for comparison, benchmarking and future research.

scholarly journals Emulation of X-ray Light-Field Cameras

2020 ◽  
Vol 6 (12) ◽  
pp. 138
Author(s):  
Nicola Viganò ◽  
Felix Lucka ◽  
Ombeline de La Rochefoucauld ◽  
Sophia Bethany Coban ◽  
Robert van Liere ◽  
...  
Keyword(s):  
Parameter Space ◽  
Field Data ◽  
Light Field ◽  
Three Dimensions ◽  
Light Fields ◽  
Single Shot ◽  
Spatial Structures ◽  
Single Exposure ◽  
X Ray ◽  
Plenoptic Imaging

X-ray plenoptic cameras acquire multi-view X-ray transmission images in a single exposure (light-field). Their development is challenging: designs have appeared only recently, and they are still affected by important limitations. Concurrently, the lack of available real X-ray light-field data hinders dedicated algorithmic development. Here, we present a physical emulation setup for rapidly exploring the parameter space of both existing and conceptual camera designs. This will assist and accelerate the design of X-ray plenoptic imaging solutions, and provide a tool for generating unlimited real X-ray plenoptic data. We also demonstrate that X-ray light-fields allow for reconstructing sharp spatial structures in three-dimensions (3D) from single-shot data.

scholarly journals Deep Recurrent Network for Fast and Full-Resolution Light Field Deblurring

2019 ◽  
Vol 26 (12) ◽  
pp. 1788-1792
Author(s):  
Jonathan Samuel Lumentut ◽  
Tae Hyun Kim ◽  
Ravi Ramamoorthi ◽  
In Kyu Park
Keyword(s):  
Light Field ◽  
Recurrent Network ◽  
Full Resolution
Sign in / Sign up

Export Citation Format

Share Document