Various deep learning-based light field image super-resolution methods have attained notable success in recent years. However, most of them focus on encoder design while neglecting the critical role of upsampling process in decoder part. Motivated by the recent progress in single image domain with implicit neural representation, we elaborately propose a spatial-angular-epipolar implicit image function (SAEIIF) in this paper, which can redefine the upsampling process to significantly improve performance and enable arbitrary-scale light field super-resolution. Specifically, it contains two complementary upsampling branches. One branch incorporates spatial implicit image function (SIIF) and angular implicit image function (AIIF) to mine intra-view information in sub-aperture images and inter-view information in macro pixels. The other branch involves epipolar implicit image function (EIIF) to leverage spatial-angular correlation in epipolar plane images. By decomposing SIIF, AIIF and EIIF into horizontal and vertical two-step upsampling to form a perfect match of upsampling scale, SAEIIF introduces a multi-stage feature interaction architecture across two branches to fully merge spatial, angular and epipolar domain information. Furthermore, we optimize feature sampling strategy based on characteristics of sub-aperture images, macro pixels, and epipolar plane images, introducing horizontal-vertical separable local sampling for SIIF and AIIF, as well as dual-source oriented line sampling used for EIIF. The extensive experimental results demonstrate that our SAEIIF can be effectively integrated with most encoders and achieve outstanding performance on both fixed-scale and arbitrary-scale light field spatial super-resolution, angular super-resolution, spatial-angular joint super-resolution.

Light field (LF) benefits various applications due to its rich spatial and angular information. To address the technical limitation in terms of imaging resolution, LF view reconstruction becomes a research hotspot. However, relevant methods mainly focus on pixel representation modeling on image plane but ignore the importance of scene geometry modeling. Inspired by powerful geometry description ability embedded in 3D Gaussian Splatting, we construct a network called LFGaussian to perform generalizable LF view reconstruction in this paper. Specifically, owing to the unique composition of cross-view Gaussian attribute deviation under 4D LF imaging setting, we propose disparity-guided feed-forward 2D Gaussian propagation with novel Gaussian primitive definition, subtly implementing Gaussian unprojection-projection operation in camera parameter-free case. On this basis, we introduce a dual-branch workflow including Gaussian representation rendering and pixel representation upsampling to create features of target views from two different levels, which complement each other to jointly realize geometric structure consistency as well as texture detail consistency across all target views. Besides, for the pursuit of high-efficient and high-quality Gaussian representation rendering, we design sub-sampling Gaussian decoding to alleviate Gaussian redundancy and leverage Gaussian splitting to allocate additional Gaussians for complex geometry regions identified by disparity gradient. Experimental results show that the proposed LFGaussian achieves superior performance compared with state-of-the-art methods on both real-world and synthetic LF datasets, proving the effectiveness of introducing Gaussian representation for LF view reconstruction. Furthermore, our LFGaussian supports arbitrary-scale reconstruction, showing high flexibility for the upsampling scale factor.