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Efficient Light Field Reconstruction via Spatio-Angular Dense Network
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DOI：10.1109/TIM.2021.3100326.png)
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Abstract
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.
Keywords:
Convolutional neural network
deep learning
image processing
light field (LF) imaging
light field
reconstruction
Journal
IF:
5.9
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1.9W
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5.8W