@article{Yang2026, 
author = {Da Yang and Hao Sheng and Sizhe Wang and Wei Ke and Zhang Xiong},
title = {Pyramid-angular-constraint network for light field super-resolution},
year = {2026},
journal = {Computational Visual Media},
volume = {12},
number = {1},
pages = {221-242},
keywords = {super-resolution, light field (LF), cross-view difference, angular–distance constraint, discriminative complementation},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450441},
doi = {10.26599/CVM.2025.9450441},
abstract = {Light field (LF) cameras record both intensity and directions of light rays in a scene with a single exposure. Due to the trade-off between spatial and angular dimensions, the spatial resolution of LF images is limited, so super-resolution is widely studied. Pixels follow linear coordinate projection across views in LF images. Hence, auxiliary views nearer to the target view are generally more effective for use in super-resolution. In this paper, an LF-pyramid is proposed based on an angular-distance constraint for discriminatively exploiting auxiliary views. From views of different layers in an LF-pyramid, complementary features of different effectiveness can be extracted. However, shapes of LF-pyramids change for target views with different angular positions. To fully exploit an LF-pyramid, we introduce a pyramid-angular-constraint network for LF super-resolution (LF-PACNet). Specifically, to handle an arbitrary number of views in each layer, an intra-pyramid-layer feature extraction module is designed, which treats all views in the same layer equally in complementary information extraction. Then, to deal with an arbitrary number of layers, a recurrent cross-pyramid-layer feature complementation module is constructed, which discriminatively complements the target view with high-frequency details. Extensive experiments on public datasets demonstrate state-of-the-art performance for our method, both visually and numerically, especially for datasets with large disparities.}
}