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The pipeline of the light field reconstruction using the proposed LightGAN model. We visualize the architecture of the high-dimensional generator and discriminator. The g... View more
Abstract: A light field image captured by a plenoptic camera can be considered a sampling of light distribution within a given space. However, with the limited pixel count of the s... View more
A light field image captured by a plenoptic camera can be considered a sampling of light distribution within a given space. However, with the limited pixel count of the sensor, the acquisition of a high-resolution sample often comes at the expense of losing parallax information. In this work, we present a learning-based generative framework to overcome such tradeoff by directly simulating the light field distribution. An important module of our model is the high-dimensional residual block, which fully exploits the spatio-angular information. By directly learning the distribution, our approach can generate both high-quality sub-aperture images and densely-sampled light fields. Experimental results on both real-world and synthetic datasets demonstrate that the proposed method outperforms other state-of-the-art approaches and achieves visually more realistic results.
Published in: IEEE Access ( Volume: 8 )
The pipeline of the light field reconstruction using the proposed LightGAN model. We visualize the architecture of the high-dimensional generator and discriminator. The g... View more
TABLE 1
Quantitative Performance of the Proposed Network Trained Using Different Loss Terms on HCI New Testset for Spatial
4\times
Task
TABLE 2
Quantitative Evaluation (PSNR) on Synthetic Light Field and Real-World Light Field for
\gamma_{s} = 4
. All Numbers are Measured in dB. Not All of the Reflective and Occlusions Scenes are Used. In This Study, We Randomly Select 20 Scenes From Each Category for Evaluation and Report of the Average PSNR Values
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In computer vision and three-dimensional imaging, light field imaging has generated a lot of interest due to the designs of various capturing systems [1] , [2] . Compared to conventional cameras, a light field camera (also known as a plenoptic camera) allows one to capture both intensity values and directions of light rays from real-world scenes. The additional information enables many applications, such as image refocusing [3] , depth estimation [4] , [5] , and novel view generation [6] , [7] . However, there is an inherent tradeoff between spatial and angular resolutions. The generally lower spatial resolution of the light field image poses great challenges in exploiting the advantages brought from additional angular sampling [8] , [9] .
Taking advantage of the parallax between two neighboring views, the captured light field scenes preserve high correlations among the sub-aperture images (SAIs). Others addressing the light field super-resolution problem generally regard geometry properties as the reconstruction priors, and warp the neighboring views to the target view [10] , [11] . The performance of these methods depends on accurate geometric information of the scene as priors. However, approaches for depth estimation have difficulties in providing accurate depth estimation for pixel warping. Errors in this process give rise to artifacts such as tearing and ghosting.
To mitigate the dependency on explicit depth or disparity information, many alternative approaches are based on sampling and consecutive reconstruction of the plenoptic function [12] , [13] . Instead of using the disparity as auxiliary information, they consider each pixel of the given SAI as a sample of the light field distribution function. Recently, deep learning has been proved to be a powerful technique in a wide range of applications [14] , [15] . With the availability of the light field dataset [16] , methods based on the convolutional neural networks (CNNs) have been successfully applied to light field super-resolution [17] , [18] . Yoon et al. [19] establish the first deep learning framework LFCNN for both spatial and angular super-resolution but do not exploit the correlation among adjacent views. Wang et al. [20] regard the light field as an image sequence and introduce the bidirectional recurrent convolutional neural network to approximate the correspondences of neighboring images. However, the image sequence assumption reduces the complexity of light field angular correlation and changes the relations among the surrounding SAIs, which consequently limits the reconstruction results.
Given the inherent geometric properties of light field data, reconstruction algorithms should involve information from both spatial and angular dimensions. However, existing methods struggle to handle the uncertainty in recovering lost high-frequency spatial details while preserving angular correlation. In this paper, we propose a generative model to effectively address the light field super-resolution problem. The generative adversarial networks (GANs) are well known for their powerful capacity in generating plausible-looking natural images with high perceptual quality [21] , [22] . Considering such benefits, we incorporate the high-dimensional convolution (HDC) l
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