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.

翻译：作为图像遥感工具,光场图像可以提供与单镜图像相比的额外角信息,并便利了广泛的测量应用。光场图像捕捉装置通常会因角分辨率和空间分辨率之间的内在权衡而受到影响。为了解决这一问题,已经提出了几种方法,例如光场重建和光场超分辨率,但是没有解决两个问题,即域不对称和高效率的信息流动。在本文件中,我们提议为光场重建建立一个端到端的Spatio-Agorn Dense 网络(SADenseNet),有两个新的组成部分,即相关区块和spatio-acronic commany 跳过连接来解决这些问题。前者以符合域不对称的方式对相关信息进行有效的建模。后者包括三种连接,加强两个领域的信息流动。对真实世界和合成数据集进行了广泛的实验,以证明拟议的SADenseNet最新性能大大降低记忆和计算成本。定性结果表明,重建后的光场图像具有精确度,可以改进相关应用的精确度。