Progressive epipolar geometry for robust light field super-resolution

Light field technology captures both spatial and angular information, densely sampled high-resolution light field images contain abundant 3-dimensional geometric information, enabling widespread applications in various industrial fields. However, existing methods for joint spatial and angular super-resolution of light field images face significant challenges when reconstructing scenes with large disparities and significant occlusions. To address this issue, we propose a geometric information enhancement module that enables efficient extraction and preservation of geometric information, and then we propose a non-disparity-based, one-stage approach that allows for the direct reconstruction of densely sampled high-resolution light field images from sparsely sampled low-resolution counterparts. Specifically, we decompose the original 4-dimensional light field image into three distinct 2-dimensional representations: spatial, angular, and epipolar plane image. Among these representations, the epipolar plane image contains abundant geometric information, which inspires us to propose a more efficient feature extractor. In addition, we introduce a progressive feature fusion strategy that better preserves geometric information extracted from epipolar plane images. Finally, to avoid errors introduced by warping operations that complicate the process of enhancing geometric information, we introduce a spatial-angular integrated upsampling module. Extensive experimental results on public datasets demonstrate that our proposed method significantly outperforms state-of-the-art approaches both quantitatively and qualitatively. Specifically, on the Occlusions dataset, our method achieved significant improvement in performance while reducing inference time by approximately 80% compared to the current best method. This efficiency gain is particularly beneficial for practical applications of the artificial intelligence algorithm.