Efficient Light Field Reconstruction via Spatio-Angular Dense Network

TL;DR

This paper introduces SADenseNet, a novel deep learning model for light field reconstruction that effectively models domain asymmetry and enhances information flow, achieving state-of-the-art results with reduced computational costs.

Contribution

The paper proposes a new end-to-end Spatio-Angular Dense Network with correlation blocks and dense skip connections to improve light field reconstruction.

Findings

Achieves state-of-the-art reconstruction quality.

Reduces memory and computational costs.

Produces sharp, detailed light field images.

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.