Efficient Light Field Reconstruction via Spatio-Angular Dense Network

Zexi Hu

doi:10.1109/TIM.2021.3100326

2021-01-01

Zexi Hu

Henry Wing Fung Yeung

Xiaoming Chen *

Yuk Ying Chung

Haisheng Li

DOI：10.1109/TIM.2021.3100326

原文链接

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摘要

En 中文

As an image sensing instrument, light field images can supply extra angular information compared with monocular images and have facilitated a wide range of measurement applications. Light field image capturing devices usually suffer from the inherent tradeoff between the angular and spatial resolutions. To tackle this problem, several methods, such as light field reconstruction and light field super-resolution, have been proposed but leaving two problems unaddressed, namely domain asymmetry and efficient information flow. In this article, we propose an end-to-end spatio-angular dense network (SADenseNet) for light field reconstruction with two novel components, namely correlation blocks and spatio-angular dense skip connections to address them. The former performs effective modeling of the correlation information in a way that conforms with the domain asymmetry. Also, the latter consists of three kinds of connections enhancing the information flow within two domains. Extensive experiments on both real-world and synthetic datasets have been conducted to demonstrate that the proposed SADenseNet's state-of-the-art performance at significantly reduced costs in memory and computation. The qualitative results show that the reconstructed light field images are sharp with correct details and can serve as preprocessing to improve the accuracy of related measurement applications.

Keyword:

Convolutional neural network

deep learning

image processing

light field (LF) imaging

light field

reconstruction

期刊

IEEE Transactions on Instrumentation and Measurement

IF:

5.9

论文数:

1.9W

被引数:

5.8W

机构

University of Sydney

学者数:

6.5W

论文数: 6.2W

被引数: 90

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