scholarly journals Three-Dimensional Reconstruction of Light Field Based on Phase Similarity

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7734
Author(s):  
Wei Feng ◽  
Junhui Gao ◽  
Tong Qu ◽  
Shiqi Zhou ◽  
Daxing Zhao
Keyword(s):  
3D Reconstruction ◽  
Light Field ◽  
Three Dimensional ◽  
Ground Truth ◽  
Depth Estimation ◽  
Estimation Algorithm ◽  
Morphological Processing ◽  
Disparity Estimation ◽  
Reconstruction Method ◽  
Disparity Map

Light field imaging plays an increasingly important role in the field of three-dimensional (3D) reconstruction because of its ability to quickly obtain four-dimensional information (angle and space) of the scene. In this paper, a 3D reconstruction method of light field based on phase similarity is proposed to increase the accuracy of depth estimation and the scope of applicability of epipolar plane image (EPI). The calibration method of the light field camera was used to obtain the relationship between disparity and depth, and the projector calibration was removed to make the experimental procedure more flexible. Then, the disparity estimation algorithm based on phase similarity was designed to effectively improve the reliability and accuracy of disparity calculation, in which the phase information was used instead of the structure tensor, and the morphological processing method was used to denoise and optimize the disparity map. Finally, 3D reconstruction of the light field was realized by combining disparity information with the calibrated relationship. The experimental results showed that the reconstruction standard deviation of the two objects was 0.3179 mm and 0.3865 mm compared with the ground truth of the measured objects, respectively. Compared with the traditional EPI method, our method can not only make EPI perform well in a single scene or blurred texture situations but also maintain good reconstruction accuracy.

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 Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6188
Author(s):  
Ségolène Rogge ◽  
Ionut Schiopu ◽  
Adrian Munteanu
Keyword(s):  
Machine Learning ◽  
Light Field ◽  
Stereo Matching ◽  
Estimation Method ◽  
Similarity Index ◽  
Depth Estimation ◽  
Residual Error ◽  
Disparity Estimation ◽  
Machine Learning Techniques ◽  
Disparity Map

The paper presents a novel depth-estimation method for light-field (LF) images based on innovative multi-stereo matching and machine-learning techniques. In the first stage, a novel block-based stereo matching algorithm is employed to compute the initial estimation. The proposed algorithm is specifically designed to operate on any pair of sub-aperture images (SAIs) in the LF image and to compute the pair’s corresponding disparity map. For the central SAI, a disparity fusion technique is proposed to compute the initial disparity map based on all available pairwise disparities. In the second stage, a novel pixel-wise deep-learning (DL)-based method for residual error prediction is employed to further refine the disparity estimation. A novel neural network architecture is proposed based on a new structure of layers. The proposed DL-based method is employed to predict the residual error of the initial estimation and to refine the final disparity map. The experimental results demonstrate the superiority of the proposed framework and reveal that the proposed method achieves an average improvement of 15.65% in root mean squared error (RMSE), 43.62% in mean absolute error (MAE), and 5.03% in structural similarity index (SSIM) over machine-learning-based state-of-the-art methods.

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 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.

scholarly journals A Novel High Efficiency Fractal Multiview Video Codec

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shiping Zhu ◽  
Dongyu Zhao ◽  
Ling Zhang
Keyword(s):  
High Efficiency ◽  
Translational Motion ◽  
Three Dimensional ◽  
Rate Distortion ◽  
Spatial Prediction ◽  
Estimation Algorithm ◽  
Video Data ◽  
Video Codec ◽  
Disparity Estimation ◽  
Multiview Video

Multiview video which is one of the main types of three-dimensional (3D) video signals, captured by a set of video cameras from various viewpoints, has attracted much interest recently. Data compression for multiview video has become a major issue. In this paper, a novel high efficiency fractal multiview video codec is proposed. Firstly, intraframe algorithm based on the H.264/AVC intraprediction modes and combining fractal and motion compensation (CFMC) algorithm in which range blocks are predicted by domain blocks in the previously decoded frame using translational motion with gray value transformation is proposed for compressing the anchor viewpoint video. Then temporal-spatial prediction structure and fast disparity estimation algorithm exploiting parallax distribution constraints are designed to compress the multiview video data. The proposed fractal multiview video codec can exploit temporal and spatial correlations adequately. Experimental results show that it can obtain about 0.36 dB increase in the decoding quality and 36.21% decrease in encoding bitrate compared with JMVC8.5, and the encoding time is saved by 95.71%. The rate-distortion comparisons with other multiview video coding methods also demonstrate the superiority of the proposed scheme.

scholarly journals UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hidetoshi Urakubo ◽  
Torsten Bullmann ◽  
Yoshiyuki Kubota ◽  
Shigeyuki Oba ◽  
Shin Ishii
Keyword(s):  
Image Segmentation ◽  
3D Reconstruction ◽  
Three Dimensional ◽  
Ground Truth ◽  
Rapid Expansion ◽  
Deep Convolutional Neural Networks ◽  
Electron Microscopic ◽  
Ground Truth Generation ◽  
3D Cnn ◽  
2D And 3D

AbstractRecently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.

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