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.

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 How depth estimation in light fields can benefit from super-resolution?

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141774844 ◽  
Author(s):  
Mandan Zhao ◽  
Gaochang Wu ◽  
Yebin Liu ◽  
Xiangyang Hao
Keyword(s):  
High Resolution ◽  
Light Field ◽  
Three Dimensional ◽  
Super Resolution ◽  
Depth Estimation ◽  
Light Fields ◽  
Trade Off ◽  
Depth Maps ◽  
Field Imaging

With the development of consumer light field cameras, the light field imaging has become an extensively used method for capturing the three-dimensional appearance of a scene. The depth estimation often requires a dense sampled light field in the angular domain or a high resolution in the spatial domain. However, there is an inherent trade-off between the angular and spatial resolutions of the light field. Recently, some studies for super-resolving the trade-off light field have been introduced. Rather than the conventional approaches that optimize the depth maps, these approaches focus on maximizing the quality of the super-resolved light field. In this article, we investigate how the depth estimation can benefit from these super-resolution methods. Specifically, we compare the qualities of the estimated depth using (a) the original sparse sampled light fields and the reconstructed dense sampled light fields, and (b) the original low-resolution light fields and the high-resolution light fields. Experiment results evaluate the enhanced depth maps using different super-resolution approaches.

scholarly journals Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xin Yang ◽  
Qingling Chang ◽  
Xinglin Liu ◽  
Siyuan He ◽  
Yan Cui
Keyword(s):  
Depth Map ◽  
Depth Estimation ◽  
Multi Scale ◽  
Monocular Depth

Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming

2011 ◽  
pp. 209-228
Author(s):  
Muhammad Tariq Mahmood ◽  
Tae-Sun Choi
Keyword(s):  
Genetic Programming ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Depth Map ◽  
Depth Estimation ◽  
Shape Reconstruction ◽  
Optical Methods ◽  
3D Shape ◽  
Focus Measure ◽  
Single Focus

Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape from focus (SFF) is one of the passive optical methods for 3D shape recovery, which uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we introduce the development of optimal composite depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is developed through optimally combining the primary information extracted using one (homogeneous features) or more focus measures (heterogeneous features). The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of this function is investigated using both synthetic and real world image sequences. Experimental results demonstrate that the proposed estimator is more accurate than existing SFF methods. Further, it is found that heterogeneous function is more effective than homogeneous function.

scholarly journals Attention-Based View Selection Networks for Light-Field Disparity Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 12095-12103
Author(s):  
Yu-Ju Tsai ◽  
Yu-Lun Liu ◽  
Ming Ouhyoung ◽  
Yung-Yu Chuang
Keyword(s):  
Light Field ◽  
State Of The Art ◽  
Depth Estimation ◽  
Disparity Estimation ◽  
View Selection ◽  
Deep Network ◽  
Depth Maps ◽  
Improve Accuracy ◽  
Light Field Image ◽  
Symmetric Property

This paper introduces a novel deep network for estimating depth maps from a light field image. For utilizing the views more effectively and reducing redundancy within views, we propose a view selection module that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation. By exploring the symmetric property of light field views, we enforce symmetry in the attention map and further improve accuracy. With the attention map, our architecture utilizes all views more effectively and efficiently. Experiments show that the proposed method achieves state-of-the-art performance in terms of accuracy and ranks the first on a popular benchmark for disparity estimation for light field images.

Structured Light Field Generated by Two Projectors for High-Speed Three Dimensional Measurement

2016 ◽  
Vol 28 (4) ◽  
pp. 523-532 ◽  
Author(s):  
Akihiro Obara ◽  
◽  
Xu Yang ◽  
Hiromasa Oku ◽  
Keyword(s):  
High Speed ◽  
Light Field ◽  
Stereo Matching ◽  
Structured Light ◽  
Three Dimensional ◽  
Depth Estimation ◽  
Image Features ◽  
Depth Information ◽  
Speed Tracking ◽  
Three Dimensional Measurement

[abstFig src='/00280004/10.jpg' width='300' text='Concept of SLF generated by two projectors' ] Triangulation is commonly used to restore 3D scenes, but its frame of less than 30 fps due to time-consuming stereo-matching is an obstacle for applications requiring that results be fed back in real time. The structured light field (SLF) our group proposed previously reduced the amount of calculation in 3D restoration, realizing high-speed measurement. Specifically, the SLF estimates depth information by projecting information on distance directly to a target. The SLF synthesized as reported, however, presents difficulty in extracting image features for depth estimation. In this paper, we propose synthesizing the SLF using two projectors with a certain layout. Our proposed SLF’s basic properties are based on an optical model. We evaluated the SLF’s performance using a prototype we developed and applied to the high-speed depth estimation of a target moving randomly at a speed of 1000 Hz. We demonstrate the target’s high-speed tracking based on high-speed depth information feedback.

Single-shot structured-light-field three-dimensional imaging

2020 ◽  
Vol 45 (12) ◽  
pp. 3256
Author(s):  
Zewei Cai ◽  
Giancarlo Pedrini ◽  
Wolfgang Osten ◽  
Xiaoli Liu ◽  
Xiang Peng
Keyword(s):  
Light Field ◽  
Structured Light ◽  
Three Dimensional ◽  
Single Shot ◽  
Three Dimensional Imaging

scholarly journals The Lightfield Microscope Eyepiece

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6619
Author(s):  
Nicolò Incardona ◽  
Ángel Tolosa ◽  
Gabriele Scrofani ◽  
Manuel Martinez-Corral ◽  
Genaro Saavedra
Keyword(s):  
3D Reconstruction ◽  
Time Resolution ◽  
Optical Design ◽  
Three Dimensional ◽  
Fourier Integral ◽  
Single Shot ◽  
Proof Of Concept ◽  
Novel Device ◽  
Translation Stage ◽  
Commercial Device

Lightfield microscopy has raised growing interest in the last few years. Its ability to get three-dimensional information about the sample in a single shot makes it suitable for many applications in which time resolution is fundamental. In this paper we present a novel device, which is capable of converting any conventional microscope into a lightfield microscope. Based on the Fourier integral microscope concept, we designed the lightfield microscope eyepiece. This is coupled to the eyepiece port, to let the user exploit all the host microscope’s components (objective turret, illumination systems, translation stage, etc.) and get a 3D reconstruction of the sample. After the optical design, a proof-of-concept device was built with off-the-shelf optomechanical components. Here, its optical performances are demonstrated, which show good matching with the theoretical ones. Then, the pictures of different samples taken with the lightfield eyepiece are shown, along with the corresponding reconstructions. We demonstrated the functioning of the lightfield eyepiece and lay the foundation for the development of a commercial device that works with any microscope.

scholarly journals EANet: Depth Estimation Based on EPI of Light Field

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yunzhang Du ◽  
Qian Zhang ◽  
Dingkang Hua ◽  
Jiaqi Hou ◽  
Bin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Medical Treatment ◽  
Light Field ◽  
Spatial Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Experimental Results ◽  
Depth Information ◽  
The Neural Network ◽  
Field Information

The light field is an important way to record the spatial information of the target scene. The purpose of this paper is to obtain depth information through the processing of light field information and provide a basis for intelligent medical treatment. In this paper, we first design an attention module to extract the features of light field images and connect all the features as a feature map to generate an attention image. Then, the attention map is integrated with the convolution layer in the neural network in the form of weights to enhance the weight of the subaperture viewpoint, which is more meaningful for depth estimation. Finally, the obtained initial depth results were optimized. The experimental results show that the MSE, PSNR, and SSIM of the depth map obtained by this method are increased by about 13%, 10 dB, and 4%, respectively, in some scenarios with good performance.

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