Efficient Light Field Reconstruction via Spatio-Angular Dense Network

• URL: http://arxiv.org/abs/2108.03635v1
• Date: Sun, 8 Aug 2021 13:50:51 GMT
• Title: Efficient Light Field Reconstruction via Spatio-Angular Dense Network
• Authors: Zexi Hu, Henry Wing Fung Yeung, Xiaoming Chen, Yuk Ying Chung, Haisheng Li
• Abstract summary: We propose an end-to-end Spatio-Angular Dense Network (SADenseNet) for light field reconstruction. We show that the proposed SADenseNet's state-of-the-art performance at significantly reduced costs in memory and computation. Results show that the reconstructed light field images are sharp with correct details and can serve as pre-processing to improve the accuracy of measurement related applications.
• Score: 14.568586050271357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 trade-off 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 paper, 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. And 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 pre-processing to improve the accuracy of related measurement applications.

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